<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>We make a unique contribution to momentum research by proposing a way to quantify momentum with performance indicators (i.e., features). We argue that due to measurable randomness in the NHL, sequential outcomes’ dependence or independence may not be the best way to approach momentum. Instead, we quantify momentum using a small sample of a team’s recent games and a linear line of best-fit to determine the trend of a team’s performances before an upcoming game. We show that with the use of SVM and logistic regression these momentum- based features have more predictive power than traditional frequency-based features in a pre-game prediction model which only uses each team’s three most recent games to assess team quality. While a random forest favors the use of both feature sets combined. The predictive power of these momentum-based features suggests that momentum is a real phenomenon in the NHL and may have more effect on the outcome of games than suggested by previous research. In addition, we believe that how our momentum-based features were designed and compared to frequency-based features could form a framework for comparing the short-term effects of momentum on any individual sport or team.</p>
</abstract>

We make a unique contribution to momentum research by proposing a way to quantify momentum with performance indicators (i.e., features). We argue that due to measurable randomness in the NHL, sequential outcomes’ dependence or independence may not be the best way to approach momentum. Instead, we quantify momentum using a small sample of a team’s recent games and a linear line of best-fit to determine the trend of a team’s performances before an upcoming game. We show that with the use of SVM and logistic regression these momentum- based features have more predictive power than traditional frequency-based features in a pre-game prediction model which only uses each team’s three most recent games to assess team quality. While a random forest favors the use of both feature sets combined. The predictive power of these momentum-based features suggests that momentum is a real phenomenon in the NHL and may have more effect on the outcome of games than suggested by previous research. In addition, we believe that how our momentum-based features were designed and compared to frequency-based features could form a framework for comparing the short-term effects of momentum on any individual sport or team.

The Use of Momentum-Inspired Features in Pre-Game Prediction Models for the Sport of Ice Hockey

Memorial University of Newfoundland and Labrador

Department of Computer Science and Media Technology

International Journal of Computer Science in Sport

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

We make a unique contribution to momentum research by proposing a way to quantify momentum with performance indicators (i.e., features). We argue that due to...

The Use of Momentum-Inspired Features in Pre-Game Prediction Models for the Sport of Ice Hockey

Online veröffentlicht: 24. Feb. 2024

Seitenbereich: 1 - 21

DOI: https://doi.org/10.2478/ijcss-2024-0001

Schlüsselwörter
MOMENTUM, ICE HOCKEY, NHL, PREDICTION, APPLIED MACHINE LEARNING

© 2024 Jordan T.P. Noel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

The Use of Momentum-Inspired Features in Pre-Game Prediction Models for the Sport of Ice Hockey

Online veröffentlicht: 24. Feb. 2024

Seitenbereich: 1 - 21

DOI: https://doi.org/10.2478/ijcss-2024-0001

SchlüsselwörterMOMENTUM, ICE HOCKEY, NHL, PREDICTION, APPLIED MACHINE LEARNING

© 2024 Jordan T.P. Noel et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Schlüsselwörter
MOMENTUM, ICE HOCKEY, NHL, PREDICTION, APPLIED MACHINE LEARNING