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Poisson Modelling and Scenario Analysis of Home and Away Performance: A Case Study of Solution FC, 2024/25
Subject area: Science,Engineering and Technology · Area of research: Optimization/Operation Research
Abstract
How much of a club’s home advantage is real, and how sure can we be of it? This paper examines Solution FC’s 2024/25 season in Southern Conference B of the Nigerian National League (NNL). The club played 20 league matches and collected 29 points, 22 of them at home. Goals scored and conceded were treated as independent Poisson counts with separate home and away rates; a negative binomial variant checked whether away scoring was overdispersed, and Bayesian inference with Monte Carlo simulation carried the uncertainty through to every reported quantity. Expected points were 2.17 per home match and 0.73 per away match. The gap of 1.40 points has a 95% credible interval of 0.51 to 2.08 and shrinks to 0.74 when the rates are pulled towards the club’s overall averages. Leave-one-out validation favoured venue-specific rates over pooled rates, but not decisively. Simulated seasons averaged about 29 points, with a wide 95% interval of 17 to 42. A scenario analysis of attacking intensity, built on an assumed trade-off between attack and defensive exposure, showed that the preferred direction depends on how steep that trade-off is; the gains were small and frequently uncertain. The study covers one club and one season and is illustrative, yet it offers a reproducible template for analysing small-sample club data.
Keywords
Bayesian inference; expected points; football analytics; home advantage; Poisson distribution
References
[1] Pollard R, Pollard G. Long-term trends in home advantage in professional team sports in North America and England (1876–2003). J Sports Sci. 2005;23(4):337–350. https://doi.org/10.1080/02640410400021559
[2] Pollard R, Gómez MA. Components of home advantage in 157 national soccer leagues worldwide. Int J Sport Exerc Psychol. 2014;12(3):218–233. https://doi.org/10.1080/1612197X.2014.888245
[3] McCarrick D, Bilalic M, Neave N, Wolfson S. Home advantage during the COVID-19 pandemic: Analyses of European football leagues. Psychol Sport Exerc. 2021;56:102013. https://doi.org/10.1016/j.psychsport.2021.102013
[4] Benz LS, Lopez MJ. Estimating the change in soccer’s home advantage during the Covid-19 pandemic using bivariate Poisson regression. AStA Adv Stat Anal. 2023;107(1–2):205–232. https://doi.org/10.1007/s10182-021-00413-9
[5] Wang S, Qin Y. The impact of crowd effects on home advantage of football matches during the COVID-19 pandemic: A systematic review. PLoS One. 2023;18(11):e0289899. https://doi.org/10.1371/journal.pone.0289899
[6] Maher MJ. Modelling association football scores. Stat Neerl. 1982;36(3):109–118. https://doi.org/10.1111/j.1467-9574.1982.tb00782.x
[7] Dixon MJ, Coles SG. Modelling association football scores and inefficiencies in the football betting market. J R Stat Soc Ser C Appl Stat. 1997;46(2):265–280. https://doi.org/10.1111/1467-9876.00065
[8] Karlis D, Ntzoufras I. Analysis of sports data by using bivariate Poisson models. J R Stat Soc Ser D Statistician. 2003;52(3):381–393. https://doi.org/10.1111/1467-9884.00366
[9] Lee J, Kim J, Kim H, Lee JS. A Bayesian approach to predict football matches with changed home advantage in spectator-free matches after the COVID-19 break. Entropy. 2022;24(3):366. https://doi.org/10.3390/e24030366
[10] Ribeiro GG, da Costa LCC, Ferreira PH, do Nascimento DC. A Bayesian approach to predict performance in football: A case study. Front Sports Act Living. 2025;7:1486928. https://doi.org/10.3389/fspor.2025.1486928
[11] Michels R, Ötting M, Karlis D. Extending the Dixon and Coles model: An application to women’s football data. arXiv:2307.02139 [Preprint]. 2023. https://arxiv.org/abs/2307.02139
[12] Beal R, Chalkiadakis G, Norman TJ, Ramchurn SD. Optimising game tactics for football. In: Proceedings of the 19th International Conference on Autonomous Agents and Multiagent Systems (AAMAS). 2020. https://arxiv.org/abs/2003.10294
[13] Solution FC Media. Solution FC Southern Conference B stats for the 2024/2025 NNL season [club season statistics and results summary]. 2025.
[14] Flashscore (incl. Flashfootball). NNL 2024/2025: Results and standings [data platform]. Nigeria > NNL. n.d.
[15] Soccerway. Nigeria NNL 2024/2025: Standings and results [data platform]. n.d. https://www.soccerway.com/nigeria/nnl/
[16] Nigeria National League. Nigeria National League [official Facebook page]. Meta Platforms. n.d.
[17] Arise News. Nigeria National 2024/2025 League to kick off November 30 with opening matches across all conferences. 2024. https://arise.tv/nigeria-national-2024-2025-league-to-kick-off-november-30-with-opening-matches-across-all-conferences
[18] Sedeaud A, De Larochelambert Q, Schipman J, Toussaint JF. The COVID-19 pandemic impact on away and home victories in soccer and rugby union. Front Sports Act Living. 2021;3:695922. https://doi.org/10.3389/fspor.2021.695922
How to cite this paper
@article{1724330,
author = {Ndibe, O. E., Unaegbu, E. N., Agu, C. M., Chikwelu, C. V.},
title = {Poisson Modelling and Scenario Analysis of Home and Away Performance: A Case Study of Solution FC, 2024/25},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {10},
number = {4},
pages = {4030-4043},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1724330.pdf},
abstract = {How much of a club’s home advantage is real, and how sure can we be of it? This paper examines Solution FC’s 2024/25 season in Southern Conference B of the Nigerian National League (NNL). The club played 20 league matches and collected 29 points, 22 of them at home. Goals scored and conceded were treated as independent Poisson counts with separate home and away rates; a negative binomial variant checked whether away scoring was overdispersed, and Bayesian inference with Monte Carlo simulation carried the uncertainty through to every reported quantity. Expected points were 2.17 per home match and 0.73 per away match. The gap of 1.40 points has a 95% credible interval of 0.51 to 2.08 and shrinks to 0.74 when the rates are pulled towards the club’s overall averages. Leave-one-out validation favoured venue-specific rates over pooled rates, but not decisively. Simulated seasons averaged about 29 points, with a wide 95% interval of 17 to 42. A scenario analysis of attacking intensity, built on an assumed trade-off between attack and defensive exposure, showed that the preferred direction depends on how steep that trade-off is; the gains were small and frequently uncertain. The study covers one club and one season and is illustrative, yet it offers a reproducible template for analysing small-sample club data.},
keywords = {Bayesian inference; expected points; football analytics; home advantage; Poisson distribution},
month = {October},
}