# Machine learning for sports betting: should model selection be based on accuracy or calibration?

arXiv: https://arxiv.org/abs/2303.06021 · HTML: https://mlsanity.com/paper/2303.06021/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 69 references that are in the map.
- First posted on arXiv 10 Mar 2023.

## Builds on (most cited first, 12 of 12)

- [On Calibration of Modern Neural Networks](https://mlsanity.com/paper/1706.04599/) (2017)
- [Measuring Calibration in Deep Learning](https://mlsanity.com/paper/1904.01685/) (2019)
- [Beyond temperature scaling: Obtaining well-calibrated multiclass probabilities with Dirichlet calibration](https://mlsanity.com/paper/1910.12656/) (2019)
- [Verified Uncertainty Calibration](https://mlsanity.com/paper/1909.10155/) (2019)
- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Improving model calibration with accuracy versus uncertainty optimization](https://mlsanity.com/paper/2012.07923/) (2020)
- [Feature Selection: A Data Perspective](https://mlsanity.com/paper/1601.07996/) (2016)
- [Hyperparameter Optimization: A Spectral Approach](https://mlsanity.com/paper/1706.00764/) (2017)
- [Estimating Expected Calibration Errors](https://mlsanity.com/paper/2109.03480/) (2021)
- [Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection](https://mlsanity.com/paper/2008.02327/) (2020)
- [On Kelly Betting: Some Limitations](https://mlsanity.com/paper/1710.01787/) (2017)
- [Optimal sports betting strategies in practice: an experimental review](https://mlsanity.com/paper/2107.08827/) (2021)

## Built on by (most cited first, 2 of 2)

- [Confident, Calibrated, or Complicit: Safety Alignment and Ideological Bias in LLM Hate Speech Detection](https://mlsanity.com/paper/2509.00673/) (2025)
- [Long-Sequence LSTM Modeling for NBA Game Outcome Prediction Using a Novel Multi-Season Dataset](https://mlsanity.com/paper/2512.08591/) (2025)

## Abstract (arXiv)

Sports betting's recent federal legalisation in the USA coincides with the golden age of machine learning. If bettors can leverage data to reliably predict the probability of an outcome, they can recognise when the bookmaker's odds are in their favour. As sports betting is a multi-billion dollar industry in the USA alone, identifying such opportunities could be extremely lucrative. Many researchers have applied machine learning to the sports outcome prediction problem, generally using accuracy to evaluate the performance of predictive models. We hypothesise that for the sports betting problem, model calibration is more important than accuracy. To test this hypothesis, we train models on NBA data over several seasons and run betting experiments on a single season, using published odds. We show that using calibration, rather than accuracy, as the basis for model selection leads to greater returns, on average (return on investment of +34.69% versus -35.17%) and in the best case (+36.93% versus +5.56%). These findings suggest that for sports betting (or any probabilistic decision-making problem), calibration is a more important metric than accuracy. Sports bettors who wish to increase profits should therefore select their predictive model based on calibration, rather than accuracy.