Sport Predictions with R

Schedule:

6.15 pm Doors open
6.30 pm Introduction / Welcome by the organizing team
6.40 pm Talks (see below)
7.45 - 8 pm Time to pitch about job opportunities & general ideas
(e v e r y o n e welcome to pitch, - DM us if you need a slot!)
ca 8 - 9 pm Beer

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"A Comparison of Covariate-based Prediction Methods for FIFA World Cups"
Andreas Groll, JProf. Dr., Datenanalyse und Statistische Algorithmen, Technische Universität Dortmund
https://arxiv.org/abs/1806.03208v3

Many approaches that analyze and predict the results of international matches in soccer are based on statistical models incorporating several potentially influential covariates with respect to a national team's success, such as the bookmakers' ratings or the FIFA ranking. Based on all matches from the four previous FIFA World Cups 2002-2014, we compare the most common regression models that are based on the teams' covariate information with regard to their predictive performances. Furthermore, an alternative modeling class is investigated, so-called random forests (Breimann, 2001).
Random forests can be seen as mixture between machine learning and statistical modeling and are known for their high predictive power. Here, we consider two different types of random forests depending on the choice of the response. One type of random forests tries to predict the precise numbers of goals while the other type considers the three match outcomes win, draw and loss using a special algorithm for ordinal response recently proposed by Hornung (2017).
For all these different modeling techniques the predictive performance with regard to several goodness-of-fit measures is compared. Based on the estimates of the best performing method all match outcomes of the FIFA World Cup 2018 in Russia are repeatedly simulated (1,000,000 times), resulting in winning probabilities for all participating national teams.

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"Predicting biathlon shooting performance using machine learning"
Thomas Maier, MSc, Wissenschaftlicher Mitarbeiter, Sportphysiologie Ausdauer, Eidgenössische Hochschule für Sport Magglingen (EHSM)
Thomas Maier, Daniel Meister, Severin Trösch & Jon Peter Wehrlin (2018) Predicting biathlon shooting performance using machine learning, Journal of Sports Sciences, 36:20, 2333-2339,
DOI: 10.1080/02640414.2018.1455261

Shooting in biathlon competitions substantially influences final rankings, but the predictability of hits and misses is unknown. The aims of the underlying study for this presentation were
A) to explore factors influencing biathlon shooting performance and
B) to predict future hits and misses.
We explored data from 118,300 shots from 4 seasons and trained various machine learning models before predicting 34,340 future shots (in the subsequent season).
A) Lower hit rates were discovered in the sprint and pursuit disciplines compared to individual and mass start (P < 0.01, h = 0.14), in standing compared to prone shooting (P < 0.01, h = 0.15) and in the 1st prone and 5th standing shot (P < 0.01, h = 0.08 and P < 0.05, h = 0.05).
B) A tree-based boosting model predicted future shots with an area under the ROC curve of 0.62, 95% CI [0.60, 0.63], slightly outperforming a simple logistic regression model and an artificial neural network (P < 0.01). The dominant predictor was an athlete’s preceding mode-specific hit rate, but a high degree of randomness persisted, which complex models could not substantially reduce. Athletes should focus on overall mode-specific hit rates which epitomise shooting skill, while other influences seem minor.

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