Sports Analytics Weekly by kubeia.io - 10/2024

Apps like DraftKings and FanDuel are expanding way beyond football and basketball.

StasBomb’s exclusive line battles data provides tremendous detail on the interactions between the offensive and defensive lines. Via this data, we now know who each player engaged with, where they were on the field when this happened, and when/where the engagement both starts and ends. This in turn allows for the creation of metrics and analysis on vital areas of game planning and prep such as run tendencies, while also allowing deeper analysis- such as this look at pockets using our free TB12 data.

We study probability forecasts in the context of cross-sectional asset pricing with a large number of firm characteristics. Empirically, we find that a simple probability forecast model can surprisingly perform as well as a sophisticated probability forecast model, and all of which deliver long-short portfolios whose Sharpe ratios are comparable to those of the widely used return forecasts. Moreover, we show that combining probability forecasts with return forecasts yields superior portfolio performance versus using each type of forecast individually, suggesting that probability forecasts provide valuable information beyond return forecasts for our understanding of the cross-section of stock returns.

The prevalence of approaches based on gradient boosted trees among the top contestants in the M5 competition is potentially the most eye-catching result. Tree-based methods out-shone other solutions, in particular deep learning-based solutions. The winners in both tracks of the M5 competition heavily relied on them. This prevalence is even more remarkable given the dominance of other methods in the literature and the M4 competition. This article tries to explain why tree-based methods were so widely used in the M5 competition. We see possibilities for future improvements of tree-based models and then distill some learnings for other approaches, including but not limited to neural networks.

Switching From Reporting p-values to Reporting Likelihood Functions Might Help Fix the Replication Crisis

Statistical process control (spc) is a robust framework for separating signal from noise. It is worth learning because it is easy, and it gives you superhuman ability to put your effort in where it’s valuable. It has made me, personally, more productive than I could have dreamt of being before it. It helps me be a better manager for my team, by not sending them on unnecessary wild goose chases, and pointing out areas for genuine improvement.