Which soccer metrics best predict winning? A data-driven analysis across Europe’s top five leagues

Which soccer metrics best predict winning? 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A data-driven analysis across Europe’s top five leagues Bishrelt Bat-Erdene, Adam Peloguin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7682022/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study tests which team-level metrics best predict success across Europe’s top five soccer leagues. Using data from the 2023–24 and 2024–25 seasons, I analyzed outcomes (goals, goal difference, points), expected metrics (xG, xGA, xGD), style indicators (possession, progression, G + A/90, xG + xAG/90), and a custom finishing efficiency measure. All work was done in Microsoft Excel using correlations, multiple regression, clustering, and residual analysis. Points per match (Pts/MP) was used to standardize success across leagues. Goal difference and expected goal difference (xGD) had the strongest relationships with Pts/MP. Chance creation metrics (xG + xAG/90 and G + A/90) were also strongly related to results in both seasons. Regression models showed defense mattered as much as attack: lower xGA consistently predicted more points. Finishing efficiency was a useful separator of elite and mid-table teams. Clustering revealed five stable play styles (high-possession progressors, controlled buildup teams, vertical creators, deep-block survivalists, and direct counters), with similar performance gaps in both seasons. At the league level, the Premier League combined higher chance creation with strong results, while Serie A and La Liga achieved similar points with fewer chances; the Bundesliga and Ligue 1 underperformed relative to chance creation. Overall, success in elite soccer comes from a balance of chance creation, defensive strength, and clinical finishing. Beyond describing team outcomes, the Excel-based workflow also shows how data can reveal consistent tactical identities across leagues and seasons. This makes the approach useful not only for comparing teams, but also for highlighting where strategies succeed or fail in different competitive environments. Finance soccer analytics sports statistics regression analysis clustering European football Figures Figure 1 Figure 2 Figure 3 Introduction The rise of soccer analytics has transformed how teams, analysts, and fans understand performance. Predicting success in soccer remains complex. Traditional statistics such as goals, shots, and possession provide some insight, yet they often fail to capture the full picture of performance. In recent years, soccer analytics has expanded rapidly, supported by publicly available data and advanced metrics (Anderson & Sally, 2013 ). Advanced metrics such as expected goals (xG) and progression statistics now allow deeper evaluation of attacking and defensive efficiency (FBref.com, 2025 ; FBref.com, n.d.; MLSSoccer.com, 2020), yet there remains debate about which indicators most reliably predict success across leagues and seasons (Mackenzie & Cushion, 2013 ; Sarmento et al., 2014 ). Previous studies have often focused on single competitions or short time frames, limiting generalizability. For example, research on the Premier League has emphasized possession and chance creation, while studies in Serie A and La Liga highlight defensive efficiency and tactical compactness (Collet, 2013 ; Rathke, 2017 ). Few analyses have compared metrics across multiple leagues using standardized data, leaving open the question of which performance indicators best capture success in different competitive contexts (Liu et al., 2016 ; Pollard & Reep, 1997 ; Sarmento et al., 2014 ). This study aims to address that gap by analyzing team-level performance across Europe’s top five leagues (Premier League, La Liga, Bundesliga, Serie A, Ligue 1) over two consecutive seasons (2023–24 and 2024–25). We evaluate both traditional and advanced key performance indicators (KPIs) using consistent methods within Microsoft Excel. Our approach combines correlation, regression, clustering, and residual analyses to identify which metrics most strongly predict points per match (Pts/MP), a standardized measure of success. By comparing across leagues and tactical styles, we aim to provide a clear framework for understanding the drivers of team performance in modern soccer. Methods Data collection I downloaded team-level performance stats from the 2023–24 and 2024–25 seasons across Europe’s top five soccer leagues (Premier League, La Liga, Bundesliga, Serie A, Ligue 1) from fbref.com. Raw tables (matches played, goals for/against, expected goals [xG], possession, progressive passes, assists, etc.) were imported into Microsoft Excel and standardized across leagues. In total, the dataset included 98 unique teams across both seasons. All metrics were aggregated at the full-season level (FBref.com, 2025 ). Key performance indicators (KPIs) We analyzed both traditional and advanced KPIs, grouped into four categories: Outcome metrics : Goals For (GF), Goals Against (GA), Goal Difference (GD), Points, and Points per Match (Pts/MP). Expected metrics : Expected Goals (xG), Expected Goals Against (xGA), Expected Goal Difference (xGD), and Expected Goals + Expected Assists per 90 minutes (xG + xAG/90). Style metrics : Possession (%), Progressive Passes (PrgP), Goals + Assists per 90 (G + A/90), xG per Possession, and Progressive Passes per Possession. Efficiency metric : To measure how well clubs converted expected chances into actual output, we created a custom Finishing Efficiency metric: $$\:Finishing\:Efficiency\:=\frac{G+A\:per\:90}{xG+xAG\:per\:90}$$ A full reference table of all KPIs used in this study is provided below (Table 1 ). Table 1 Key performance indicators analyzed. KPI Category Description GF Outcome Goals For (total) GA Outcome Goals Against (total) GD Outcome Goal Difference (GF – GA) Pts Outcome Total Points in the season Pts/MP Outcome Points per Match xG Expected Expected Goals xGA Expected Expected Goals Against xGD Expected Expected Goal Difference xG + xAG/90 Expected Expected Goals plus Expected Assists per 90 minutes Poss Style Possession Percentage PrgP (Progression) Style Total Progressive Passes G + A/90 Style Goals plus Assists per 90 minutes xG per Poss Style Expected Goals per unit of Possession % Prog Passes per Poss Style Progressive Passes per unit of Possession % Finishing Efficiency Efficiency Ratio of actual output (G + A per 90) to expected output (xG + xAG per 90) Derived metrics (Excel formulas) Key variables were calculated directly in Excel: Pts/MP : =Total Points / Matches Played Finishing Efficiency : (Goals + Assists per 90) / (xG + xAG per 90) Percentile ranking : =PERCENTRANK.INC(Range, Cell) to rank teams within distributions Quartile thresholds : =PERCENTILE.INC(Range, 0.25) and = PERCENTILE.INC(Range, 0.75) defined cutoffs for “Low,” “Mid,” and “High” buckets Efficiency bucket formula (example) : =IF( $ B2 < 0.97,"Low",IF( $ B2 < = 1.02,"Mid","High")) Style labels : Possession, progression, and efficiency buckets were concatenated (e.g., =BH2&"-"&BI2) to generate interpretable team profiles such as High-possession progressors or Direct counters . Statistical analyses I used MS Excel for the following multiple analyses to assess relationships between KPIs and team success (measured by Pts/MP): Correlation : =CORREL(Y-range, X-range) tested pairwise KPI associations. Multiple regression : Conducted using Excel’s Regression tool within the Data Analysis add-in. Outputs included coefficients, standard errors, t-statistics, p-values, and R² values. Residuals were calculated by subtracting predicted Pts/MP from observed values. Clustering : Teams were grouped into five stylistic clusters (high-possession progressors, controlled buildup teams, vertical creators, deep-block survivalists, and direct counters) using percentile thresholds for possession, progression, and efficiency. Residual analysis : Compared observed vs. expected Pts/MP to identify over- and under-performers relative to KPI-based models. Excel analysis tools Pivot tables : Aggregated averages (e.g., Pts/MP, possession, efficiency) by league and cluster. Charts : Scatterplots (e.g., xG + xAG/90 vs Pts/MP, with bubble size = possession, color = efficiency) and residual plots. Team names were overlaid using Format Data Labels → Value from Cells . Conditional formatting : Applied to highlight efficiency levels and cluster differences in visual interpretation. Results Elite teams consistently turned dominance into points Points per match (Pts/MP) was used as the main point of reference across both seasons, since it standardizes team success despite leagues having different total match counts across the top five leagues. Values ranged from 0.32 (Southampton, 2024–25) to 2.65 (Leverkusen, 2023–24). The clubs at the top of each league table were those with the highest Pts/MP, showing a direct link between this metric and league position. Table 2 Elite vs. relegation-level teams by points per match (Pts/MP). Season Highest Pts/MP (Elite club) Pts/MP Lowest Pts/MP (Relegation club) Pts/MP 2023–24 Leverkusen (Bundesliga) 2.65 Granada (La Liga) 0.55 2024–25 Liverpool (Premier League) 2.21 Southampton (Premier League) 0.32 This comparison highlights the gap between elite and struggling teams. While playing styles differed, Pts/MP clearly separated top performers from relegation-threatened clubs. Chance creation was the strongest predictor of success When comparing different key performance indicators (KPIs) to points per match (Pts/MP), both seasons showed that creating and converting chances mattered more than possession or efficiency alone. In 2023–24, the strongest correlations with Pts/MP were goal difference (r = 0.97), expected goal difference (xGD, r = 0.91), and chance creation measured by G + A per 90 (r = 0.85) and xG + xAG per 90 (r = 0.83). In 2024–25, the pattern held: goal difference (r = 0.97) and xGD (r = 0.93) were the best predictors, with G + A per 90 (r = 0.87) and xG + xAG per 90 (r = 0.84) also strongly matching success. Possession and progression showed moderate but meaningful correlations (r = 0.73–0.83), while finishing efficiency was relatively weaker (r = 0.50). These results show that teams consistently generating high-quality chances and maintaining strong expected goal differences were much more likely to succeed, regardless of possession dominance. Table 3 Correlation of key metrics with points per match (Pts/MP). Correlation coefficients (r) for outcome, expected, and playstyle metrics across the 2023–24 and 2024–25 seasons. Chance creation (xG + xAG/90 and G + A/90) consistently showed the strongest link with success, while possession and finishing efficiency were weaker predictors. KPI Correlation_with_Pts/MP 2023-24 season 2024-25 season GD 0.97 0.97 xGD 0.91 0.93 G + A/90 0.85 0.87 PrgP (Progression) 0.83 0.81 Poss 0.78 0.73 xG + xAG/90 0.83 0.84 Finishing_Efficiency 0.50 0.50 Efficiency separated the champions from the rest Regression models were used to test which KPIs predicted success, measured by Points per Match (Pts/MP). Across both seasons, Goal Difference (GD) had the strongest simple correlation with Pts/MP (r = 0.97), but regression models were needed to see which advanced metrics still mattered when considered together. In 2023–24 , the clearest predictor was defensive strength. Lower expected goals against (xGA) was highly significant (p < 0.001), meaning teams that allowed fewer quality chances were consistently near the top. When xGA was removed from the model, ball progression (PrgP) became significant (p = 0.034), and possession showed a borderline effect (p = 0.075). Finishing efficiency was not significant in either model. In 2024–25 , finishing efficiency emerged as an important factor. Teams that converted chances more effectively gained a measurable advantage (p = 0.046 with xGA; p = 0.038 without xGA). Progression was again significant (p = 0.046 with xGA; p = 0.003 without xGA), and xGA remained highly predictive (p < 0.001). Chance creation (xG) trended toward significance in the model without xGA (p = 0.068), but did not fully cross the threshold. These results suggest that while creating chances is necessary, the clubs at the very top were separated by defensive stability and efficient finishing. Liverpool (2024–25) and Leverkusen (2023–24) both combined those traits, while underperformers such as Manchester United and Sevilla failed to turn solid KPI profiles into consistent points. Table 4 Key predictors of team success (Pts/MP) from regression models. Season Significant predictors (p < 0.05) Notes 2023–24 xGA (lower values = stronger defense), PrgP (Progression) Defensive solidity was the strongest predictor; progression added some value, but finishing efficiency was not significant. 2024–25 xGA (lower values = stronger defense), PrgP (Progression), Finishing Efficiency Defense remained crucial, but finishing efficiency also separated top clubs from others. Full regression outputs with coefficients, t-statistics, and p-values are provided in the Excel file. Finishing efficiency was the great divider To measure how well clubs converted expected chances into actual output, I created a custom Finishing Efficiency metric: $$\:Finishing\:Efficiency\:=\frac{G+A\:per\:90}{xG+xAG\:per\:90}$$ A value above 1.0 indicates clinical conversion (more goals/assists than expected), while values below 1.0 reflect underperformance. This measure was necessary because traditional xG-based measures capture chance quality but not whether teams consistently finished those chances (Davis & Robberechts, 2024 ). Across both seasons, teams with finishing efficiency above 1.0 regularly outperformed expectations. Liverpool, Real Madrid, Arsenal, and Girona turned their chance creation into top point totals. In contrast, Sevilla, Burnley, and Granada underperformed, leaving them in mid-table or relegation battles despite similar xG profiles. Five clear playstyle clusters emerged across Europe Teams across both seasons were grouped into five distinct clusters based on possession, progression, and efficiency: High-possession progressors (e.g., Man City, Real Madrid, PSG, Bayern) – these clubs consistently paired heavy possession with elite finishing, averaging ~2.1 Pts/MP and ranking near the top of their leagues. Controlled buildup teams (e.g., Napoli, Roma, Lille, Bologna) – patient and balanced possession produced solid but not elite outcomes, averaging ~1.7 Pts/MP. Vertical creators (e.g., Atalanta, Monaco, Atlético) – direct forward play produced mid-table success (~1.8 Pts/MP). Deep-block survivalists (e.g., Dortmund, Newcastle, Lyon, Aston Villa) – defensive setups averaged just ~1.2 Pts/MP, with many teams fighting to stay in mid-table. Direct counters (e.g., Everton, Union Berlin, Cagliari, Ipswich Town) – reactive low-possession play was the least effective, averaging ~0.9 Pts/MP and league ranks close to relegation. Table 5 Cluster style summaries across two seasons. Cluster / Style Avg Pts/MP Avg League Rank Notes 2023–24 2024–25 2023–24 2024–25 Controlled buildup teams 1.7 ~ 1.7 5.3 ~ 6.0 Elite : Bologna, Napoli; Mid-table : Roma, Lille, Real Sociedad Deep-block survivalists 1.2 ~ 1.2 11.4 ~ 11.0 Mid-table : Dortmund, Villa, Newcastle; Relegation: Lecce, Granada, Monza Direct counters 0.9 ~ 0.9 14.8 ~ 14.5 Relegation : Everton, Cagliari, Union Berlin, Ipswich High-possession progressors 2.1 ~ 2.1 3.8 ~ 3.5 Elite : Man City, Arsenal, Liverpool, Real Madrid, Barcelona, Bayern, Leverkusen, Inter, Chelsea Vertical creators 1.8 ~ 1.8 5.0 ~ 5.0 Mid-table : Atalanta, Monaco, Atlético, Lens The same five clusters appeared in both seasons, and their performance gaps were nearly identical. This consistency highlights that the framework captures stable tactical identities across Europe, rather than short-term variation. Some clubs beat the numbers, others fell short Residual analysis compared actual performance (Pts/MP) with predicted values from regression models. This highlighted which clubs gained more or fewer points than expected based on their KPI profiles. In 2023–24, Juventus, Inter, and Atlético Madrid were among the top overperformers, consistently earning more points than predicted. Brest and Nice also stood out in Ligue 1 as teams that converted efficient play into stronger results. On the other hand, Almería and Burnley were clear underperformers, and even Bayern Munich earned fewer points than expected despite strong metrics. In 2024–25, Napoli recorded the highest positive residual, showing that their results went well beyond what the models predicted. Freiburg and Fiorentina also outperformed expectations, while Roma and Rayo Vallecano added further surprises. By contrast, Tottenham fell far below its predicted values, making it the biggest underperformer. Saint-Étienne and Rennes in Ligue 1, and Holstein Kiel in the Bundesliga, also underachieved relative to their underlying metrics. This analysis highlights how intangibles like coaching, mentality, and squad balance can push teams above or below their statistical expectations. Table 6 Top 5 overperformers and underperformers by residuals (2023–24 and 2024–25). Residuals = Actual Pts/MP – Predicted Pts/MP. Positive values show teams that outperformed their KPI profile, while negative values show underperformance. Season Top 5 Overperformers (highest residuals) Residual Top 5 Underperformers (lowest residuals) Residual 2023–24 Juventus (Serie A) + 0.52 Almería (La Liga) –0.45 Nice (Ligue 1) + 0.45 Burnley (Premier League) –0.40 Inter (Serie A) + 0.41 Bayern Munich (Bundesliga) –0.38 Atlético Madrid (La Liga) + 0.37 Salernitana (Serie A) –0.38 Brest (Ligue 1) + 0.35 Darmstadt 98 (Bundesliga) –0.37 2024–25 Napoli (Serie A) + 0.61 Tottenham (Premier League) –0.65 Freiburg (Bundesliga) + 0.32 Holstein Kiel (Bundesliga) –0.44 Fiorentina (Serie A) + 0.31 Saint-Étienne (Ligue 1) –0.41 Rayo Vallecano (La Liga) + 0.30 Rennes (Ligue 1) –0.39 Roma (Serie A) + 0.30 Ipswich Town (Premier League) –0.36 League style profiles League-wide comparisons showed distinct stylistic differences across Europe’s top five leagues. Table 7 and Fig. 3 A-B summarize how finishing efficiency, expected goals plus expected assists (xG + xAG), possession, and results (Pts/MP) aligned. In 2023–24 , the Premier League led in attacking production (2.83 xG + xAG/90) and possession (51.4%), and also achieved the highest points per match (1.52). La Liga and Serie A had lower xG + xAG (2.29 and 2.19) but maintained similar points per match (1.48 and 1.46), suggesting greater efficiency in turning fewer chances into results. Ligue 1 lagged, with both lower finishing efficiency (0.94) and fewer points (1.40). The Bundesliga created many chances (2.66 xG + xAG) but returned a modest 1.42 Pts/MP, indicating underperformance relative to output. In 2024–25 , the Premier League again topped both chance creation (2.65 xG + xAG) and results (1.53 Pts/MP). Serie A and La Liga converged at 1.48 Pts/MP despite Serie A producing fewer chances (2.17 vs. 2.31 xG + xAG), again pointing to greater efficiency. Ligue 1 improved slightly (1.45 Pts/MP) but remained behind, while the Bundesliga stayed the least efficient, averaging just 1.41 Pts/MP despite high xG + xAG. Taken together, these patterns highlight two consistent themes: (1) the Premier League combined volume (chances and possession) with results, while (2) Serie A and La Liga showed efficiency in converting fewer chances into similar points. By contrast, the Bundesliga and Ligue 1 underperformed relative to their chance creation. Table 7 League averages of style metrics and results. Season League Finishing efficiency xG + xAG/90 Poss (%) Pts/MP 2023–24 Bundesliga 1.04 2.66 50.3 1.42 La Liga 0.99 2.29 50.9 1.48 Ligue 1 0.94 2.36 50.1 1.40 Premier League 1.01 2.83 51.4 1.52 Serie A 1.01 2.19 50.7 1.46 2024–25 Bundesliga 1.05 2.53 50.3 1.41 La Liga 0.97 2.31 50.8 1.48 Ligue 1 0.97 2.59 50.3 1.45 Premier League 1.01 2.65 50.9 1.53 Serie A 1.03 2.17 50.9 1.48 Discussion This study demonstrates that chance creation, defensive solidity, and finishing efficiency are the primary drivers of success across Europe’s top five leagues. Goal difference unsurprisingly correlated almost perfectly with points per match, but more detailed analyses revealed consistent patterns that extend beyond outcomes alone. First, both goal difference and expected goal difference (xGD) were the strongest predictors of team success, each showing very high correlations with points per match. In addition, chance creation metrics such as xG + xAG per 90 and G + A per 90 also strongly matched performance, showing that teams that consistently generated high-quality chances were best positioned to succeed. This supports prior work linking passing sequences and shot creation to match outcomes (Hughes & Franks, 2005 ; Rathke, 2017 ). This also aligns with prior research emphasizing expected goals as a measure of attacking strength, and our results confirm that combining xG with assists (xAG) provides added predictive value (Sarmento et al., 2014 ; Mackenzie & Cushion, 2013 ). Second, regression analyses showed that defense was equally decisive. Lower expected goals against (xGA) consistently predicted higher points per match, confirming that elite clubs separate themselves not only by scoring but also by limiting the quality of chances conceded. Liverpool (2024–25) and Leverkusen (2023–24) exemplified this balance, while underperforming teams such as Sevilla and Manchester United struggled to convert solid attacking metrics into results due to defensive instability. Third, finishing efficiency emerged as a key differentiator. While not the strongest single predictor, it consistently explained why certain clubs exceeded or fell short of their expected values. Teams such as Real Madrid, Arsenal, and Girona outperformed models due to clinical finishing, while Burnley and Granada failed to convert chance creation into points. This suggests that efficiency, though variable, remains critical for separating champions from mid-table sides (Davis & Robberechts, 2024 ). Tactical clustering reinforced these findings. High-possession progressors dominated across both seasons, consistently averaging over 2 points per match. Direct counters, by contrast, struggled with fewer than 1 point per match on average. The stability of these five stylistic groups across consecutive seasons indicates that the framework captures enduring tactical identities rather than short-term fluctuations (Pollard & Reep, 1997 ), consistent with earlier work showing that the value of possession varies with situational factors such as scoreline and opposition quality (Collet, 2013 ; Lago-Peñas & Dellal, 2010 ). Finally, league-level comparisons revealed meaningful differences in style. The Premier League combined the highest attacking volume with the strongest results, while Serie A and La Liga demonstrated efficiency by converting fewer chances into similar outcomes. In contrast, the Bundesliga and Ligue 1 generated many chances but underperformed relative to expectations, suggesting structural or tactical inefficiencies. These cross-league differences emphasize that the relationship between metrics and success is shaped by broader competitive environments (FBref.com, 2025 ; Liu et al., 2016 ). Overall, our results suggest that success in elite soccer is best explained by a combination of chance creation, defensive stability, and clinical finishing. These findings provide both a framework for academic analysis and practical insights for coaches, analysts, and recruitment departments. Conclusion This analysis of Europe’s top five soccer leagues across two seasons shows that team success depends on more than possession or attacking volume. The strongest predictors of winning were expected goal difference (xGD), chance creation (xG + xAG/90 and G + A/90), defensive solidity (xGA), and finishing efficiency. Together, these metrics explain why elite clubs consistently outperform others and why certain teams exceeded or fell short of statistical expectations. By applying a simple, reproducible Excel-based framework, we show that tactical styles and league-wide differences can be systematically compared. While high-possession progressors consistently dominated, defensive and efficient teams in Serie A and La Liga also achieved strong outcomes. These results highlight that winning in modern soccer requires not only creating chances but also defending effectively and finishing clinically. Limitations This study has several limitations. All data came from fbref.com tables, which may not capture every tactical detail. Analyses were conducted only in Microsoft Excel, without advanced modeling, and the focus was limited to team-level metrics across just two seasons. Player-level variation, coaching, and broader contextual factors such as scheduling and finances were not considered. Declarations Acknowledgements I would like to thank my advisor and AP Statistics teacher, Mr. Adam Peloguin, for his valuable guidance on this project. I am also grateful to Newton North High School for its academic support and encouragement, and to my family for their constant support. References Anderson, C., & Sally, D. (2013). The numbers game: Why everything you know about soccer is wrong. Penguin Books. Collet, C. (2013). The possession game? A comparative analysis of ball retention and team success in European and international football, 2007–2010. Journal of Sports Sciences, 31 (2), 123–136. https://doi.org/10.1080/02640414.2012.727455 Davis, J., & Robberechts, P. (2024). Biases in expected goals models confound finishing ability . arXiv preprint arXiv:2401.09940. https://arxiv.org/abs/2401.09940 FBref.com. (2025). Team statistics for Europe’s top five leagues, 2023–24 and 2024–25 seasons. Retrieved August 2025, from https://fbref.com/en/ FBref.com. (n.d.). Expected goals (xG) explained. Retrieved August 2025, from https://fbref.com/en/expected-goals-model-explained/ Hughes, M., & Franks, I. (2005). Analysis of passing sequences, shots and goals in soccer. Journal of Sports Sciences, 23 (5), 509–514. https://doi.org/10.1080/02640410410001716779 Lago-Peñas, C., & Dellal, A. (2010). Ball possession strategies in elite soccer according to the evolution of the match-score: The influence of situational variables. Journal of Human Kinetics, 25 (1), 93–100. https://doi.org/10.2478/v10078-010-0036-z Liu, H., Hopkins, W., & Gómez, M. Á. (2016). Modelling relationships between match events and match outcome in elite football. European Journal of Sport Science, 16 (5), 516–525. https://doi.org/10.1080/17461391.2015.1042527 Mackenzie, R., & Cushion, C. (2013). Performance analysis in football: A critical review and implications for future research. Journal of Sports Sciences, 31 (6), 639–676. https://doi.org/10.1080/02640414.2012.746720 MLSSoccer.com. (2020, April 16). Soccer analytics 101: Expected goals. https://www.mlssoccer.com/news/soccer-analytics-101 Pollard, R., & Reep, C. (1997). Measuring the effectiveness of playing strategies at soccer. The Statistician, 46 (4), 541–550. https://www.jstor.org/stable/2988603 Rathke, A. (2017). An examination of expected goals and shot efficiency in soccer. Journal of Human Sport and Exercise, 12 (2), 514–529. https://doi.org/10.14198/jhse.2017.122.15 Sarmento, H., Marcelino, R., Anguera, M. T., Campaniço, J., Matos, N., & Leitão, J. C. (2014). Match analysis in football: A systematic review. Journal of Sports Sciences, 32 (20), 1831–1843. https://doi.org/10.1080/02640414.2014.898852 Additional Declarations The authors declare no competing interests. Supplementary Files 202324Season.xlsx 2023-24 season complete dataset and analysis 202425Season.xlsx 2024-25 season complete dataset and analysis Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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09:18:35","extension":"xml","order_by":9,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":74554,"visible":true,"origin":"","legend":"","description":"","filename":"rs76820220structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/d79bd25cfa2eb5424a476133.xml"},{"id":92069815,"identity":"9864e04f-89ea-409f-9766-bf23cdc01aba","added_by":"auto","created_at":"2025-09-24 09:34:35","extension":"html","order_by":10,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":82930,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/a555fb6939a91711095a546b.html"},{"id":92068837,"identity":"d9814d20-17ac-455e-b8da-d7b58acec55b","added_by":"auto","created_at":"2025-09-24 09:26:35","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":296832,"visible":true,"origin":"","legend":"\u003cp\u003eFinishing efficiency and team success across Europe’s top five leagues.\u003cbr\u003e\n\u003cstrong\u003e(A)\u003c/strong\u003e Scatterplots of finishing efficiency vs. points per match (2023–24 and 2024–25). Teams above the 1.0 line were efficient finishers; those below underperformed relative to their chance quality. \u003cstrong\u003e(B)\u003c/strong\u003e Top five and bottom five teams by finishing efficiency in each season, highlighting how clinical finishing separated elite performers from struggling clubs.\u003c/p\u003e","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/b7a5d387efb95dc841003fcc.jpg"},{"id":92068834,"identity":"570e2a3a-f7cf-4630-9cfd-5e0fd2e7425e","added_by":"auto","created_at":"2025-09-24 09:26:35","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":519999,"visible":true,"origin":"","legend":"\u003cp\u003eCluster styles and average performance across Europe’s top five leagues (A: 2023–24 and B: 2024–25).\u003cbr\u003e\nBar and line charts show average Pts/MP (bars) and average league rank (line) for each of the five clusters. High-possession progressors consistently dominated, while direct counters struggled at the bottom.\u003c/p\u003e","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/8b77d3e010f79763e1973daf.jpg"},{"id":92067867,"identity":"54d28370-c9a0-4320-b631-e4331105e627","added_by":"auto","created_at":"2025-09-24 09:18:35","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":410909,"visible":true,"origin":"","legend":"\u003cp\u003eLeague style profiles (A: 2023–24, B: 2024–25).\u003cbr\u003e\n\u003cem\u003eBubble charts showing average league styles. x-axis: xG+xAG per 90; y-axis: points per match (Pts/MP). Bubble size = possession; color = finishing efficiency. The Premier League consistently combined the highest chance creation with strong results. Serie A and La Liga achieved similar points with fewer chances, while the Bundesliga generated high xG but underperformed. Ligue 1 trailed in both seasons.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/48b394366ab7dbabffed6014.jpg"},{"id":92070940,"identity":"648e4a61-28f9-4e5c-83d6-d780d45bdbfa","added_by":"auto","created_at":"2025-09-24 09:46:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2329901,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/94b89889-5e41-4729-9561-a4b5b219c718.pdf"},{"id":92067866,"identity":"0ccba407-850d-4c38-9186-031e1ae13e57","added_by":"auto","created_at":"2025-09-24 09:18:35","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":131174,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2023-24 season complete dataset and analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"202324Season.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/897526a967a627b063aaf80d.xlsx"},{"id":92069814,"identity":"5435dfe0-51ac-4b75-ae1a-61f048906363","added_by":"auto","created_at":"2025-09-24 09:34:35","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":132806,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e2024-25 season complete dataset and analysis\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"202425Season.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-7682022/v1/17c8ed4aec0e97ff5d51b62c.xlsx"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eWhich soccer metrics best predict winning? A data-driven analysis across Europe’s top five leagues\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe rise of soccer analytics has transformed how teams, analysts, and fans understand performance. Predicting success in soccer remains complex. Traditional statistics such as goals, shots, and possession provide some insight, yet they often fail to capture the full picture of performance. In recent years, soccer analytics has expanded rapidly, supported by publicly available data and advanced metrics (Anderson \u0026amp; Sally, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Advanced metrics such as expected goals (xG) and progression statistics now allow deeper evaluation of attacking and defensive efficiency (FBref.com, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; FBref.com, n.d.; MLSSoccer.com, 2020), yet there remains debate about which indicators most reliably predict success across leagues and seasons (Mackenzie \u0026amp; Cushion, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sarmento et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003ePrevious studies have often focused on single competitions or short time frames, limiting generalizability. For example, research on the Premier League has emphasized possession and chance creation, while studies in Serie A and La Liga highlight defensive efficiency and tactical compactness (Collet, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Rathke, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Few analyses have compared metrics across multiple leagues using standardized data, leaving open the question of which performance indicators best capture success in different competitive contexts (Liu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Pollard \u0026amp; Reep, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e; Sarmento et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThis study aims to address that gap by analyzing team-level performance across Europe\u0026rsquo;s top five leagues (Premier League, La Liga, Bundesliga, Serie A, Ligue 1) over two consecutive seasons (2023\u0026ndash;24 and 2024\u0026ndash;25). We evaluate both traditional and advanced key performance indicators (KPIs) using consistent methods within Microsoft Excel. Our approach combines correlation, regression, clustering, and residual analyses to identify which metrics most strongly predict points per match (Pts/MP), a standardized measure of success. By comparing across leagues and tactical styles, we aim to provide a clear framework for understanding the drivers of team performance in modern soccer.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eData collection\u003c/h2\u003e\u003cp\u003eI downloaded team-level performance stats from the 2023\u0026ndash;24 and 2024\u0026ndash;25 seasons across Europe\u0026rsquo;s top five soccer leagues (Premier League, La Liga, Bundesliga, Serie A, Ligue 1) from fbref.com. Raw tables (matches played, goals for/against, expected goals [xG], possession, progressive passes, assists, etc.) were imported into Microsoft Excel and standardized across leagues. In total, the dataset included 98 unique teams across both seasons. All metrics were aggregated at the full-season level (FBref.com, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003eKey performance indicators (KPIs)\u003c/h3\u003e\n\u003cp\u003eWe analyzed both traditional and advanced KPIs, grouped into four categories:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eOutcome metrics\u003c/b\u003e: Goals For (GF), Goals Against (GA), Goal Difference (GD), Points, and Points per Match (Pts/MP).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eExpected metrics\u003c/b\u003e: Expected Goals (xG), Expected Goals Against (xGA), Expected Goal Difference (xGD), and Expected Goals\u0026thinsp;+\u0026thinsp;Expected Assists per 90 minutes (xG\u0026thinsp;+\u0026thinsp;xAG/90).\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStyle metrics\u003c/b\u003e: Possession (%), Progressive Passes (PrgP), Goals\u0026thinsp;+\u0026thinsp;Assists per 90 (G\u0026thinsp;+\u0026thinsp;A/90), xG per Possession, and Progressive Passes per Possession.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEfficiency metric\u003c/b\u003e: To measure how well clubs converted expected chances into actual output, we created a custom Finishing Efficiency metric:\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:Finishing\\:Efficiency\\:=\\frac{G+A\\:per\\:90}{xG+xAG\\:per\\:90}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA full reference table of all KPIs used in this study is provided below (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eKey performance indicators analyzed.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eKPI\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCategory\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eDescription\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGF\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoals For (total)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoals Against (total)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoal Difference (GF \u0026ndash; GA)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePts\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Points in the season\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePts/MP\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eOutcome\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePoints per Match\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exG\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExpected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected Goals\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exGA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExpected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected Goals Against\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exGD\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExpected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected Goal Difference\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exG\u0026thinsp;+\u0026thinsp;xAG/90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eExpected\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected Goals plus Expected Assists per 90 minutes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePoss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStyle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003ePossession Percentage\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePrgP (Progression)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStyle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eTotal Progressive Passes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eG\u0026thinsp;+\u0026thinsp;A/90\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStyle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eGoals plus Assists per 90 minutes\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003exG per Poss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStyle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eExpected Goals per unit of Possession %\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eProg Passes per Poss\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eStyle\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eProgressive Passes per unit of Possession %\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFinishing Efficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eEfficiency\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u003cp\u003eRatio of actual output (G\u0026thinsp;+\u0026thinsp;A per 90) to expected output (xG\u0026thinsp;+\u0026thinsp;xAG per 90)\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\n\u003ch3\u003eDerived metrics (Excel formulas)\u003c/h3\u003e\n\u003cp\u003eKey variables were calculated directly in Excel:\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePts/MP\u003c/b\u003e: =Total Points / Matches Played\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eFinishing Efficiency\u003c/b\u003e: (Goals\u0026thinsp;+\u0026thinsp;Assists per 90) / (xG\u0026thinsp;+\u0026thinsp;xAG per 90)\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePercentile ranking\u003c/b\u003e: =PERCENTRANK.INC(Range, Cell) to rank teams within distributions\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eQuartile thresholds\u003c/b\u003e: =PERCENTILE.INC(Range, 0.25) and =\u0026thinsp;PERCENTILE.INC(Range, 0.75) defined cutoffs for \u0026ldquo;Low,\u0026rdquo; \u0026ldquo;Mid,\u0026rdquo; and \u0026ldquo;High\u0026rdquo; buckets\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eEfficiency bucket formula (example)\u003c/b\u003e: =IF(\u003cspan\u003e$\u003c/span\u003eB2\u0026thinsp;\u0026lt;\u0026thinsp;0.97,\"Low\",IF(\u003cspan\u003e$\u003c/span\u003eB2\u0026thinsp;\u0026lt;\u0026thinsp;=\u0026thinsp;1.02,\"Mid\",\"High\"))\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eStyle labels\u003c/b\u003e: Possession, progression, and efficiency buckets were concatenated (e.g., =BH2\u0026amp;\"-\"\u0026amp;BI2) to generate interpretable team profiles such as \u003cem\u003eHigh-possession progressors\u003c/em\u003e or \u003cem\u003eDirect counters\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003eStatistical analyses\u003c/h3\u003e\n\u003cp\u003eI used MS Excel for the following multiple analyses to assess relationships between KPIs and team success (measured by Pts/MP):\u003c/p\u003e\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCorrelation\u003c/b\u003e: =CORREL(Y-range, X-range) tested pairwise KPI associations.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eMultiple regression\u003c/b\u003e: Conducted using Excel\u0026rsquo;s Regression tool within the \u003cem\u003eData Analysis\u003c/em\u003e add-in. Outputs included coefficients, standard errors, t-statistics, p-values, and R\u0026sup2; values. Residuals were calculated by subtracting predicted Pts/MP from observed values.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eClustering\u003c/b\u003e: Teams were grouped into five stylistic clusters (high-possession progressors, controlled buildup teams, vertical creators, deep-block survivalists, and direct counters) using percentile thresholds for possession, progression, and efficiency.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eResidual analysis\u003c/b\u003e: Compared observed vs. expected Pts/MP to identify over- and under-performers relative to KPI-based models.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e\n\u003ch3\u003eExcel analysis tools\u003c/h3\u003e\n\u003cp\u003e\u003cul\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003ePivot tables\u003c/b\u003e: Aggregated averages (e.g., Pts/MP, possession, efficiency) by league and cluster.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eCharts\u003c/b\u003e: Scatterplots (e.g., xG\u0026thinsp;+\u0026thinsp;xAG/90 vs Pts/MP, with bubble size\u0026thinsp;=\u0026thinsp;possession, color\u0026thinsp;=\u0026thinsp;efficiency) and residual plots. Team names were overlaid using \u003cem\u003eFormat Data Labels \u0026rarr; Value from Cells\u003c/em\u003e.\u003c/p\u003e\u003c/li\u003e\u003cli\u003e\u003cp\u003e\u003cb\u003eConditional formatting\u003c/b\u003e: Applied to highlight efficiency levels and cluster differences in visual interpretation.\u003c/p\u003e\u003c/li\u003e\u003c/ul\u003e\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e\n \u003ch2\u003eElite teams consistently turned dominance into points\u003c/h2\u003e\n \u003cp\u003ePoints per match (Pts/MP) was used as the main point of reference across both seasons, since it standardizes team success despite leagues having different total match counts across the top five leagues. Values ranged from 0.32 (Southampton, 2024\u0026ndash;25) to 2.65 (Leverkusen, 2023\u0026ndash;24). The clubs at the top of each league table were those with the highest Pts/MP, showing a direct link between this metric and league position.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eElite vs. relegation-level teams by points per match (Pts/MP).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eHighest Pts/MP (Elite club)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePts/MP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLowest Pts/MP (Relegation club)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePts/MP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLeverkusen (Bundesliga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGranada (La Liga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiverpool (Premier League)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSouthampton (Premier League)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eThis comparison highlights the gap between elite and struggling teams. While playing styles differed, Pts/MP clearly separated top performers from relegation-threatened clubs.\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eChance creation was the strongest predictor of success\u003c/h3\u003e\n\u003cp\u003eWhen comparing different key performance indicators (KPIs) to points per match (Pts/MP), both seasons showed that creating and converting chances mattered more than possession or efficiency alone.\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003e\n \u003cp\u003eIn 2023\u0026ndash;24, the strongest correlations with Pts/MP were goal difference (r\u0026thinsp;=\u0026thinsp;0.97), expected goal difference (xGD, r\u0026thinsp;=\u0026thinsp;0.91), and chance creation measured by G\u0026thinsp;+\u0026thinsp;A per 90 (r\u0026thinsp;=\u0026thinsp;0.85) and xG\u0026thinsp;+\u0026thinsp;xAG per 90 (r\u0026thinsp;=\u0026thinsp;0.83).\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003eIn 2024\u0026ndash;25, the pattern held: goal difference (r\u0026thinsp;=\u0026thinsp;0.97) and xGD (r\u0026thinsp;=\u0026thinsp;0.93) were the best predictors, with G\u0026thinsp;+\u0026thinsp;A per 90 (r\u0026thinsp;=\u0026thinsp;0.87) and xG\u0026thinsp;+\u0026thinsp;xAG per 90 (r\u0026thinsp;=\u0026thinsp;0.84) also strongly matching success.\u003c/p\u003e\n \u003c/li\u003e\n \u003cli\u003e\n \u003cp\u003ePossession and progression showed moderate but meaningful correlations (r\u0026thinsp;=\u0026thinsp;0.73\u0026ndash;0.83), while finishing efficiency was relatively weaker (r\u0026thinsp;=\u0026thinsp;0.50).\u003c/p\u003e\n \u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003eThese results show that teams consistently generating high-quality chances and maintaining strong expected goal differences were much more likely to succeed, regardless of possession dominance.\u003c/p\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCorrelation of key metrics with points per match (Pts/MP). Correlation coefficients (r) for outcome, expected, and playstyle metrics across the 2023\u0026ndash;24 and 2024\u0026ndash;25 seasons. Chance creation (xG\u0026thinsp;+\u0026thinsp;xAG/90 and G\u0026thinsp;+\u0026thinsp;A/90) consistently showed the strongest link with success, while possession and finishing efficiency were weaker predictors.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eKPI\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eCorrelation_with_Pts/MP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2023-24 season\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e2024-25 season\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003exGD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eG\u0026thinsp;+\u0026thinsp;A/90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.85\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePrgP (Progression)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.81\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePoss\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003exG\u0026thinsp;+\u0026thinsp;xAG/90\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFinishing_Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003c/p\u003e\n\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eEfficiency separated the champions from the rest\u003c/h2\u003e\n \u003cp\u003eRegression models were used to test which KPIs predicted success, measured by Points per Match (Pts/MP). Across both seasons, Goal Difference (GD) had the strongest simple correlation with Pts/MP (r\u0026thinsp;=\u0026thinsp;0.97), but regression models were needed to see which advanced metrics still mattered when considered together.\u003c/p\u003e\n \u003cp\u003eIn \u003cstrong\u003e2023\u0026ndash;24\u003c/strong\u003e, the clearest predictor was defensive strength. Lower expected goals against (xGA) was highly significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), meaning teams that allowed fewer quality chances were consistently near the top. When xGA was removed from the model, ball progression (PrgP) became significant (p\u0026thinsp;=\u0026thinsp;0.034), and possession showed a borderline effect (p\u0026thinsp;=\u0026thinsp;0.075). Finishing efficiency was not significant in either model.\u003c/p\u003e\n \u003cp\u003eIn \u003cstrong\u003e2024\u0026ndash;25\u003c/strong\u003e, finishing efficiency emerged as an important factor. Teams that converted chances more effectively gained a measurable advantage (p\u0026thinsp;=\u0026thinsp;0.046 with xGA; p\u0026thinsp;=\u0026thinsp;0.038 without xGA). Progression was again significant (p\u0026thinsp;=\u0026thinsp;0.046 with xGA; p\u0026thinsp;=\u0026thinsp;0.003 without xGA), and xGA remained highly predictive (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Chance creation (xG) trended toward significance in the model without xGA (p\u0026thinsp;=\u0026thinsp;0.068), but did not fully cross the threshold.\u003c/p\u003e\n \u003cp\u003eThese results suggest that while creating chances is necessary, the clubs at the very top were separated by defensive stability and efficient finishing. Liverpool (2024\u0026ndash;25) and Leverkusen (2023\u0026ndash;24) both combined those traits, while underperformers such as Manchester United and Sevilla failed to turn solid KPI profiles into consistent points.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eKey predictors of team success (Pts/MP) from regression models.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSignificant predictors (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNotes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003exGA (lower values\u0026thinsp;=\u0026thinsp;stronger defense), PrgP (Progression)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefensive solidity was the strongest predictor; progression added some value, but finishing efficiency was not significant.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003exGA (lower values\u0026thinsp;=\u0026thinsp;stronger defense), PrgP (Progression), Finishing Efficiency\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDefense remained crucial, but finishing efficiency also separated top clubs from others.\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003cp\u003eFull regression outputs with coefficients, t-statistics, and p-values are provided in the Excel file.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eFinishing efficiency was the great divider\u003c/h2\u003e\n \u003cp\u003eTo measure how well clubs converted expected chances into actual output, I created a custom Finishing Efficiency metric:\u003c/p\u003e\n \u003cdiv id=\"Equb\" class=\"Equation\"\u003e\n \u003cdiv class=\"mathdisplay\" id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:Finishing\\:Efficiency\\:=\\frac{G+A\\:per\\:90}{xG+xAG\\:per\\:90}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eA value above \u003cstrong\u003e1.0\u003c/strong\u003e indicates clinical conversion (more goals/assists than expected), while values below 1.0 reflect underperformance. This measure was necessary because traditional xG-based measures capture chance quality but not whether teams consistently finished those chances (Davis \u0026amp; Robberechts, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\n \u003cp\u003eAcross both seasons, teams with finishing efficiency above 1.0 regularly outperformed expectations. Liverpool, Real Madrid, Arsenal, and Girona turned their chance creation into top point totals. In contrast, Sevilla, Burnley, and Granada underperformed, leaving them in mid-table or relegation battles despite similar xG profiles.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003eFive clear playstyle clusters emerged across Europe\u003c/h2\u003e\n \u003cp\u003eTeams across both seasons were grouped into five distinct clusters based on possession, progression, and efficiency:\u003c/p\u003e\n \u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003e\u003cstrong\u003eHigh-possession progressors\u003c/strong\u003e (e.g., Man City, Real Madrid, PSG, Bayern) \u0026ndash; these clubs consistently paired heavy possession with elite finishing, averaging ~2.1 Pts/MP and ranking near the top of their leagues.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eControlled buildup teams\u003c/strong\u003e (e.g., Napoli, Roma, Lille, Bologna) \u0026ndash; patient and balanced possession produced solid but not elite outcomes, averaging ~1.7 Pts/MP.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eVertical creators\u003c/strong\u003e (e.g., Atalanta, Monaco, Atl\u0026eacute;tico) \u0026ndash; direct forward play produced mid-table success (~1.8 Pts/MP).\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDeep-block survivalists\u003c/strong\u003e (e.g., Dortmund, Newcastle, Lyon, Aston Villa) \u0026ndash; defensive setups averaged just ~1.2 Pts/MP, with many teams fighting to stay in mid-table.\u003c/li\u003e\n \u003cli\u003e\u003cstrong\u003eDirect counters\u003c/strong\u003e (e.g., Everton, Union Berlin, Cagliari, Ipswich Town) \u0026ndash; reactive low-possession play was the least effective, averaging ~0.9 Pts/MP and league ranks close to relegation.\u003c/li\u003e\n \u003c/ol\u003e\n \u003cdiv class=\"gridtable\"\u003e\n \u003cdiv align=\"char\" class=\"colspec\"\u003e\u003cbr\u003e\u003c/div\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eCluster style summaries across two seasons.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eCluster / Style\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAvg Pts/MP\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eAvg League Rank\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eNotes\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u0026ndash;24\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024\u0026ndash;25\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2023\u0026ndash;24\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003e2024\u0026ndash;25\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eControlled buildup teams\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;1.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;6.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElite\u003c/strong\u003e: Bologna, Napoli; \u003cstrong\u003eMid-table\u003c/strong\u003e: Roma, Lille, Real Sociedad\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeep-block survivalists\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;1.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;11.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMid-table\u003c/strong\u003e: Dortmund, Villa, Newcastle; Relegation: Lecce, Granada, Monza\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDirect counters\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;0.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;14.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRelegation\u003c/strong\u003e: Everton, Cagliari, Union Berlin, Ipswich\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHigh-possession progressors\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;2.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;3.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eElite\u003c/strong\u003e: Man City, Arsenal, Liverpool, Real Madrid, Barcelona, Bayern, Leverkusen, Inter, Chelsea\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVertical creators\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;1.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e~\u0026thinsp;5.0\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMid-table\u003c/strong\u003e: Atalanta, Monaco, Atl\u0026eacute;tico, Lens\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThe same five clusters appeared in both seasons, and their performance gaps were nearly identical. This consistency highlights that the framework captures stable tactical identities across Europe, rather than short-term variation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eSome clubs beat the numbers, others fell short\u003c/h2\u003e\n \u003cp\u003eResidual analysis compared actual performance (Pts/MP) with predicted values from regression models. This highlighted which clubs gained more or fewer points than expected based on their KPI profiles.\u003c/p\u003e\n \u003cp\u003eIn 2023\u0026ndash;24, Juventus, Inter, and Atl\u0026eacute;tico Madrid were among the top overperformers, consistently earning more points than predicted. Brest and Nice also stood out in Ligue 1 as teams that converted efficient play into stronger results. On the other hand, Almer\u0026iacute;a and Burnley were clear underperformers, and even Bayern Munich earned fewer points than expected despite strong metrics.\u003c/p\u003e\n \u003cp\u003eIn 2024\u0026ndash;25, Napoli recorded the highest positive residual, showing that their results went well beyond what the models predicted. Freiburg and Fiorentina also outperformed expectations, while Roma and Rayo Vallecano added further surprises. By contrast, Tottenham fell far below its predicted values, making it the biggest underperformer. Saint-\u0026Eacute;tienne and Rennes in Ligue 1, and Holstein Kiel in the Bundesliga, also underachieved relative to their underlying metrics.\u003c/p\u003e\n \u003cp\u003eThis analysis highlights how intangibles like coaching, mentality, and squad balance can push teams above or below their statistical expectations.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTop 5 overperformers and underperformers by residuals (2023\u0026ndash;24 and 2024\u0026ndash;25). Residuals\u0026thinsp;=\u0026thinsp;Actual Pts/MP \u0026ndash; Predicted Pts/MP. Positive values show teams that outperformed their KPI profile, while negative values show underperformance.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop 5 Overperformers (highest residuals)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTop 5 Underperformers (lowest residuals)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eResidual\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e2023\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eJuventus (Serie A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAlmer\u0026iacute;a (La Liga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNice (Ligue 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBurnley (Premier League)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInter (Serie A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBayern Munich (Bundesliga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAtl\u0026eacute;tico Madrid (La Liga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSalernitana (Serie A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.38\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBrest (Ligue 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDarmstadt 98 (Bundesliga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.37\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"5\"\u003e\n \u003cp\u003e2024\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNapoli (Serie A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTottenham (Premier League)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.65\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFreiburg (Bundesliga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.32\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHolstein Kiel (Bundesliga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.44\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFiorentina (Serie A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSaint-\u0026Eacute;tienne (Ligue 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRayo Vallecano (La Liga)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRennes (Ligue 1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.39\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRoma (Serie A)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e+\u0026thinsp;0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIpswich Town (Premier League)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026ndash;0.36\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eLeague style profiles\u003c/h2\u003e\n \u003cp\u003eLeague-wide comparisons showed distinct stylistic differences across Europe\u0026rsquo;s top five leagues. Table \u003cspan class=\"InternalRef\"\u003e7\u003c/span\u003e and Fig. \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA-B summarize how finishing efficiency, expected goals plus expected assists (xG\u0026thinsp;+\u0026thinsp;xAG), possession, and results (Pts/MP) aligned.\u003c/p\u003e\n \u003cp\u003eIn \u003cstrong\u003e2023\u0026ndash;24\u003c/strong\u003e, the Premier League led in attacking production (2.83 xG\u0026thinsp;+\u0026thinsp;xAG/90) and possession (51.4%), and also achieved the highest points per match (1.52). La Liga and Serie A had lower xG\u0026thinsp;+\u0026thinsp;xAG (2.29 and 2.19) but maintained similar points per match (1.48 and 1.46), suggesting greater efficiency in turning fewer chances into results. Ligue 1 lagged, with both lower finishing efficiency (0.94) and fewer points (1.40). The Bundesliga created many chances (2.66 xG\u0026thinsp;+\u0026thinsp;xAG) but returned a modest 1.42 Pts/MP, indicating underperformance relative to output.\u003c/p\u003e\n \u003cp\u003eIn \u003cstrong\u003e2024\u0026ndash;25\u003c/strong\u003e, the Premier League again topped both chance creation (2.65 xG\u0026thinsp;+\u0026thinsp;xAG) and results (1.53 Pts/MP). Serie A and La Liga converged at 1.48 Pts/MP despite Serie A producing fewer chances (2.17 vs. 2.31 xG\u0026thinsp;+\u0026thinsp;xAG), again pointing to greater efficiency. Ligue 1 improved slightly (1.45 Pts/MP) but remained behind, while the Bundesliga stayed the least efficient, averaging just 1.41 Pts/MP despite high xG\u0026thinsp;+\u0026thinsp;xAG.\u003c/p\u003e\n \u003cp\u003eTaken together, these patterns highlight two consistent themes: (1) the Premier League combined volume (chances and possession) with results, while (2) Serie A and La Liga showed efficiency in converting fewer chances into similar points. By contrast, the Bundesliga and Ligue 1 underperformed relative to their chance creation.\u003c/p\u003e\n \u003cp\u003e\u003c/p\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eLeague averages of style metrics and results.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSeason\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eLeague\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFinishing efficiency\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003exG\u0026thinsp;+\u0026thinsp;xAG/90\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePoss (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePts/MP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2023\u0026ndash;24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBundesliga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.66\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLa Liga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLigue 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.40\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePremier League\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerie A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2024\u0026ndash;25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBundesliga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.53\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.41\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLa Liga\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLigue 1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePremier League\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.53\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSerie A\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e50.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study demonstrates that chance creation, defensive solidity, and finishing efficiency are the primary drivers of success across Europe\u0026rsquo;s top five leagues. Goal difference unsurprisingly correlated almost perfectly with points per match, but more detailed analyses revealed consistent patterns that extend beyond outcomes alone.\u003c/p\u003e\u003cp\u003eFirst, both goal difference and expected goal difference (xGD) were the strongest predictors of team success, each showing very high correlations with points per match. In addition, chance creation metrics such as xG\u0026thinsp;+\u0026thinsp;xAG per 90 and G\u0026thinsp;+\u0026thinsp;A per 90 also strongly matched performance, showing that teams that consistently generated high-quality chances were best positioned to succeed. This supports prior work linking passing sequences and shot creation to match outcomes (Hughes \u0026amp; Franks, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Rathke, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). This also aligns with prior research emphasizing expected goals as a measure of attacking strength, and our results confirm that combining xG with assists (xAG) provides added predictive value (Sarmento et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Mackenzie \u0026amp; Cushion, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eSecond, regression analyses showed that defense was equally decisive. Lower expected goals against (xGA) consistently predicted higher points per match, confirming that elite clubs separate themselves not only by scoring but also by limiting the quality of chances conceded. Liverpool (2024\u0026ndash;25) and Leverkusen (2023\u0026ndash;24) exemplified this balance, while underperforming teams such as Sevilla and Manchester United struggled to convert solid attacking metrics into results due to defensive instability.\u003c/p\u003e\u003cp\u003eThird, finishing efficiency emerged as a key differentiator. While not the strongest single predictor, it consistently explained why certain clubs exceeded or fell short of their expected values. Teams such as Real Madrid, Arsenal, and Girona outperformed models due to clinical finishing, while Burnley and Granada failed to convert chance creation into points. This suggests that efficiency, though variable, remains critical for separating champions from mid-table sides (Davis \u0026amp; Robberechts, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eTactical clustering reinforced these findings. High-possession progressors dominated across both seasons, consistently averaging over 2 points per match. Direct counters, by contrast, struggled with fewer than 1 point per match on average. The stability of these five stylistic groups across consecutive seasons indicates that the framework captures enduring tactical identities rather than short-term fluctuations (Pollard \u0026amp; Reep, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e1997\u003c/span\u003e), consistent with earlier work showing that the value of possession varies with situational factors such as scoreline and opposition quality (Collet, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Lago-Pe\u0026ntilde;as \u0026amp; Dellal, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eFinally, league-level comparisons revealed meaningful differences in style. The Premier League combined the highest attacking volume with the strongest results, while Serie A and La Liga demonstrated efficiency by converting fewer chances into similar outcomes. In contrast, the Bundesliga and Ligue 1 generated many chances but underperformed relative to expectations, suggesting structural or tactical inefficiencies. These cross-league differences emphasize that the relationship between metrics and success is shaped by broader competitive environments (FBref.com, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Liu et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOverall, our results suggest that success in elite soccer is best explained by a combination of chance creation, defensive stability, and clinical finishing. These findings provide both a framework for academic analysis and practical insights for coaches, analysts, and recruitment departments.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis analysis of Europe\u0026rsquo;s top five soccer leagues across two seasons shows that team success depends on more than possession or attacking volume. The strongest predictors of winning were expected goal difference (xGD), chance creation (xG\u0026thinsp;+\u0026thinsp;xAG/90 and G\u0026thinsp;+\u0026thinsp;A/90), defensive solidity (xGA), and finishing efficiency. Together, these metrics explain why elite clubs consistently outperform others and why certain teams exceeded or fell short of statistical expectations.\u003c/p\u003e\u003cp\u003eBy applying a simple, reproducible Excel-based framework, we show that tactical styles and league-wide differences can be systematically compared. While high-possession progressors consistently dominated, defensive and efficient teams in Serie A and La Liga also achieved strong outcomes. These results highlight that winning in modern soccer requires not only creating chances but also defending effectively and finishing clinically.\u003c/p\u003e\u003cdiv id=\"Sec18\" class=\"Section2\"\u003e\u003ch2\u003eLimitations\u003c/h2\u003e\u003cp\u003eThis study has several limitations. All data came from fbref.com tables, which may not capture every tactical detail. Analyses were conducted only in Microsoft Excel, without advanced modeling, and the focus was limited to team-level metrics across just two seasons. Player-level variation, coaching, and broader contextual factors such as scheduling and finances were not considered.\u003c/p\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAcknowledgements\u003c/h2\u003e\u003cp\u003eI would like to thank my advisor and AP Statistics teacher, Mr. Adam Peloguin, for his valuable guidance on this project. I am also grateful to Newton North High School for its academic support and encouragement, and to my family for their constant support.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAnderson, C., \u0026amp; Sally, D. (2013). \u003cem\u003eThe numbers game: Why everything you know about soccer is wrong.\u003c/em\u003e Penguin Books.\u003c/li\u003e\n\u003cli\u003eCollet, C. (2013). The possession game? A comparative analysis of ball retention and team success in European and international football, 2007\u0026ndash;2010. \u003cem\u003eJournal of Sports Sciences, 31\u003c/em\u003e(2), 123\u0026ndash;136. https://doi.org/10.1080/02640414.2012.727455\u003c/li\u003e\n\u003cli\u003eDavis, J., \u0026amp; Robberechts, P. (2024). \u003cem\u003eBiases in expected goals models confound finishing ability\u003c/em\u003e. arXiv preprint arXiv:2401.09940. https://arxiv.org/abs/2401.09940\u003c/li\u003e\n\u003cli\u003eFBref.com. (2025). \u003cem\u003eTeam statistics for Europe\u0026rsquo;s top five leagues, 2023\u0026ndash;24 and 2024\u0026ndash;25 seasons.\u003c/em\u003e Retrieved August 2025, from https://fbref.com/en/\u003c/li\u003e\n\u003cli\u003eFBref.com. (n.d.). \u003cem\u003eExpected goals (xG) explained.\u003c/em\u003e Retrieved August 2025, from https://fbref.com/en/expected-goals-model-explained/\u003c/li\u003e\n\u003cli\u003eHughes, M., \u0026amp; Franks, I. (2005). Analysis of passing sequences, shots and goals in soccer. \u003cem\u003eJournal of Sports Sciences, 23\u003c/em\u003e(5), 509\u0026ndash;514. https://doi.org/10.1080/02640410410001716779\u003c/li\u003e\n\u003cli\u003eLago-Pe\u0026ntilde;as, C., \u0026amp; Dellal, A. (2010). Ball possession strategies in elite soccer according to the evolution of the match-score: The influence of situational variables. \u003cem\u003eJournal of Human Kinetics, 25\u003c/em\u003e(1), 93\u0026ndash;100. https://doi.org/10.2478/v10078-010-0036-z\u003c/li\u003e\n\u003cli\u003eLiu, H., Hopkins, W., \u0026amp; G\u0026oacute;mez, M. \u0026Aacute;. (2016). Modelling relationships between match events and match outcome in elite football. \u003cem\u003eEuropean Journal of Sport Science, 16\u003c/em\u003e(5), 516\u0026ndash;525. https://doi.org/10.1080/17461391.2015.1042527\u003c/li\u003e\n\u003cli\u003eMackenzie, R., \u0026amp; Cushion, C. (2013). Performance analysis in football: A critical review and implications for future research. \u003cem\u003eJournal of Sports Sciences, 31\u003c/em\u003e(6), 639\u0026ndash;676. https://doi.org/10.1080/02640414.2012.746720\u003c/li\u003e\n\u003cli\u003eMLSSoccer.com. (2020, April 16). \u003cem\u003eSoccer analytics 101: Expected goals.\u003c/em\u003e https://www.mlssoccer.com/news/soccer-analytics-101\u003c/li\u003e\n\u003cli\u003ePollard, R., \u0026amp; Reep, C. (1997). Measuring the effectiveness of playing strategies at soccer. \u003cem\u003eThe Statistician, 46\u003c/em\u003e(4), 541\u0026ndash;550. https://www.jstor.org/stable/2988603\u003c/li\u003e\n\u003cli\u003eRathke, A. (2017). An examination of expected goals and shot efficiency in soccer. \u003cem\u003eJournal of Human Sport and Exercise, 12\u003c/em\u003e(2), 514\u0026ndash;529. https://doi.org/10.14198/jhse.2017.122.15\u003c/li\u003e\n\u003cli\u003eSarmento, H., Marcelino, R., Anguera, M. T., Campani\u0026ccedil;o, J., Matos, N., \u0026amp; Leit\u0026atilde;o, J. C. (2014). Match analysis in football: A systematic review. \u003cem\u003eJournal of Sports Sciences, 32\u003c/em\u003e(20), 1831\u0026ndash;1843. https://doi.org/10.1080/02640414.2014.898852\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Newton North High School, MA, USA","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"soccer analytics, sports statistics, regression analysis, clustering, European football","lastPublishedDoi":"10.21203/rs.3.rs-7682022/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7682022/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study tests which team-level metrics best predict success across Europe\u0026rsquo;s top five soccer leagues. Using data from the 2023\u0026ndash;24 and 2024\u0026ndash;25 seasons, I analyzed outcomes (goals, goal difference, points), expected metrics (xG, xGA, xGD), style indicators (possession, progression, G\u0026thinsp;+\u0026thinsp;A/90, xG\u0026thinsp;+\u0026thinsp;xAG/90), and a custom finishing efficiency measure. All work was done in Microsoft Excel using correlations, multiple regression, clustering, and residual analysis. Points per match (Pts/MP) was used to standardize success across leagues.\u003c/p\u003e\u003cp\u003eGoal difference and expected goal difference (xGD) had the strongest relationships with Pts/MP. Chance creation metrics (xG\u0026thinsp;+\u0026thinsp;xAG/90 and G\u0026thinsp;+\u0026thinsp;A/90) were also strongly related to results in both seasons. Regression models showed defense mattered as much as attack: lower xGA consistently predicted more points. Finishing efficiency was a useful separator of elite and mid-table teams. Clustering revealed five stable play styles (high-possession progressors, controlled buildup teams, vertical creators, deep-block survivalists, and direct counters), with similar performance gaps in both seasons. At the league level, the Premier League combined higher chance creation with strong results, while Serie A and La Liga achieved similar points with fewer chances; the Bundesliga and Ligue 1 underperformed relative to chance creation.\u003c/p\u003e\u003cp\u003eOverall, success in elite soccer comes from a balance of chance creation, defensive strength, and clinical finishing. Beyond describing team outcomes, the Excel-based workflow also shows how data can reveal consistent tactical identities across leagues and seasons. This makes the approach useful not only for comparing teams, but also for highlighting where strategies succeed or fail in different competitive environments.\u003c/p\u003e","manuscriptTitle":"Which soccer metrics best predict winning? A data-driven analysis across Europe’s top five leagues","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-09-24 09:18:30","doi":"10.21203/rs.3.rs-7682022/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"c48e0e12-77fd-4746-a611-572476b12f6c","owner":[],"postedDate":"September 24th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":55143175,"name":"Finance"}],"tags":[],"updatedAt":"2025-09-24T09:18:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-09-24 09:18:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7682022","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7682022","identity":"rs-7682022","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}