Volume 21, Issue 3

When considering future performance in sport, age is an important feature for prediction models. On average, players tend to improve from their rookie (earliest) season, plateau, and then decline in performance until they retire from the league. In this paper we apply Functional Principal Component Analysis to the careers of players from the National Hockey League in order to construct individual aging curves. The approach is nonparametric in the sense that a parametric structure is not imposed on the aging curves. A main aspect of our work is the consideration of selection bias whereby players who have long careers are not randomly sampled but tend to be exceptional players. Whereas the literature constructs aging curves that represent the average player, we produce aging curves for individual players; this is particularly useful in roster construction.

Overtaking in Formula 1 was far more abundant during the 1980s than during the 2000s, but it is poorly understood what exactly caused this decline. This paper uses a negative-binomial model on overtaking data at the driver level from 1983 to 2010 to disentangle the effect of quantifiable factors, such as the number of cars and pit strategies, from the non-quantifiable overtake-friendliness of the tracks and the cars. It was found that overtaking was easiest at the beginning of the study period (1984 and 1985) and then declined until the late-1990s. While most of this decline was gradual, some abrupt downturns were discovered. These downturns seem to coincide with fuel-limit restrictions in the 1980s and the re-introduction of in-race refueling in 1994. The decline in overtaking during the 1990s may be attributed to the increased role of aerodynamics and technology, but their impact was more gradual and therefore harder to establish.

What factors contribute to the success of national teams in the Men’s Ice Hockey World Championships and the Men’s Ice Hockey World Junior Championships? This study examines whether hosting the tournament provides a home advantage; the influence of past tournament performances; the impact of players’ physical characteristics such as height, weight, and age; and the value of experience from the World Championships compared to the NHL and other leagues. We employ a dynamic ranking model based on the Plackett–Luce distribution with time-varying strength parameters driven by the score. The results show that experience in the IIHF tournaments outweighs experience in the NHL. Furthermore, in junior championships, there is a significant home advantage, and shorter, heavier players tend to have an edge. In senior championships, future success is linked to past achievements in both junior and senior championships, with younger teams performing better. Finally, we conduct a forecasting analysis to predict the probabilities of winning the tournament, earning a medal, and advancing to the playoff phase.

This work presents and evaluates the ranking algorithm that has been used by Fédération Internationale de Volleyball (FIVB) since 2020. The prominent feature of the FIVB ranking is the use of the probabilistic model, which explicitly calculates the probabilities of the future matches results using the estimated teams’ strengths. Such explicit modeling is new in the context of official sport rankings, especially for multi-level outcomes, and we study the optimality of its parameters using both analytical and numerical methods. We conclude that from the modeling perspective, the current thresholds fit well the data but adding the home-field advantage (HFA) would be beneficial. Regarding the algorithm itself, we explain the rationale behind the approximations currently used and show a simple method to find new parameters (numerical score) which improve the performance. We also show that the weighting of the match results is counterproductive.

In this study, we investigate data from the Indian Premier League (IPL) spanning from its inception in 2008 to the most recent 2024 season to identify and analyze key factors influencing cricket scores. Using the H2O AutoML framework, we develop a predictive model focused on identifying low first-innings scores, incorporating data on location, weather conditions, teams, and players, while distinguishing them from matches with par or high score. Explainable AI (XAI) tools are employed to quantify the influence of various match features on score predictions, ensuring transparency in the model’s decision-making process. To further enhance classification performance, we introduce pre-match pitch report descriptions generated by a Large Language Model (LLM). For a subset of matches, we leverage multimodal LLM capabilities to analyse pitch report videos, comparing their predictive value against textual descriptions. Our findings underscore the potential of AI and machine learning in sports analytics, specifically in predicting cricket scores based on pitch conditions and other influential factors. This research provides valuable insights for teams, coaches, fantasy sports enthusiasts, IPL administrators and analysts, helping to optimize strategies based on available pre-match information. As part of our work we are sharing a pitch report dataset, python source code for the predictive model with explainability, and a Most Valuable Player (MVP) implementation framework to enhance reproducibility and support further research in cricket analytics.