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Journal of Quantitative Analysis in Sports

An official journal of the American Statistical Association

Editor-in-Chief: Rigdon, Steve

Editorial Board Member: Glickman, PhD, Mark

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CiteScore 2016: 0.44

SCImago Journal Rank (SJR) 2015: 0.288
Source Normalized Impact per Paper (SNIP) 2015: 0.358

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1559-0410
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Measures of tactical efficiency in water polo

James Graham / John Mayberry
Published Online: 2014-02-21 | DOI: https://doi.org/10.1515/jqas-2013-0127

Abstract

We present a notational analysis of offensive tactics commonly employed in elite men’s water polo and address three questions related to this objective: which tactics are most effective?, which tactical performance indicators best classify the winning team?, and how accurate are predictive models based on these performance indicators? We define a new statistic, Efficiency Rating, which quantifies the importance of a tactic via a weighted average of direct and indirect goals generated by its use. By this measure, direct shot is the most efficient even strategy despite being employed far less frequently than centre or perimeter tactics. We address our second question by measuring the effect size of winning over losing teams for 25 tactical variables and find that exclusion conversion rate is the most effective discriminatory statistic in both close and unbalanced games, correctly classifying almost 90% of all contests. To address our third question, we develop and apply a simple Binomial model based on goals generated per play which correctly predicts all eight games in the medal round of the 2012 Men’s Olympics from preliminary rounds. Success probabilities are computed based on a weighted average of offensive and defensive efficiency with an optimal weight that favors defense.

Keywords: classification; notational analysis; prediction; water polo

References

  • Argudo Itturiaga, F., J. Roque, P. Marin, and E. Lara. 2007. “Influence of the Efficacy Values in Counterattack and Defensive Adjustment on the Condition of Winner and Loser in Male and Female Water Polo.” International Journal of Performance Analysis in Sport 7(2):81–91.Google Scholar

  • Argudo Itturriaga, F., J. Arias, E. Ruize, and J. Alonso. 2009. “Were there Differences in Tactical Efficacy Between the Winning and Losing Teams and the Final Classification in the 2003 Water Polo World Championship?.” Journal of Human Sport and Exercise 4(2):142–153.Google Scholar

  • Argudo Itturriaga, F., J. Arias, E. Ruize, and J. Alonso. 2011. “Effect of First Ball Possession on Partial and Final Scores in 2003, 2005 and 2007 Water Polo Championship.” Perceptual and Motor Skills 112(2):349–352.Web of ScienceGoogle Scholar

  • Delen, D., D. Cogdell, P. Marin, and E. Lara. 2012. “A Comparative Analysis of Data Mining Methods in Predicting Ncaa Bowl Outcomes.” International Journal of Forecasting 28(2): 543–522.CrossrefWeb of ScienceGoogle Scholar

  • Dixon, F. and W. Massey. 1951. An Introduction to Statistical Analysis. New York: McGraw-Hill.Google Scholar

  • Durrett, R. 2009. Elementary Probability for Applications. New York: Cambridge University Press.Google Scholar

  • Enomoto, I., M. Suga, M. Takahashi, M. Komori, T. Minami, H. Fujimoto, M. Saito, S. Suzuki, and J. Takahashi. 2003. “A Notational Match Analysis of the 2001 Women’S Water Polo World Championships.” Proceeding of Biomechanics and Medicine in Swimming IX, Saint-Etienne, University of Saint Etienne 487–492.Google Scholar

  • Escalante, Y., J. Saavedra, M. Mansilla, and V. Tella. 2011. “Discriminatory Power of Water Polo Game-Related Statistics at the 2008 Olympic Games.” Journal of Sports Sciences 29(3): 291–298.CrossrefWeb of ScienceGoogle Scholar

  • Escalante, Y., J. Saavedra, V. Tella, M. Mansilla, A. García-Hermoso, and A. Dominguez. 2012. “Water Polo Game-Related Statistics in Women’s International Championships: Differences and Discriminatory Power.” Journal of Sports Science and Medicine 11:475–482.Google Scholar

  • Escalante, Y., J. Saavedra, V. Tella, M. Mansilla, A. García-Hermoso, and A. Dominguez. 2013. “Differences and Discriminatory Power of Water Polo Game-Related Statistics in Men in International Championships and their Relationship with the Phase of the Competition.” The Journal of Strength and Conditioning Research 27(4):893–901.Web of ScienceCrossrefGoogle Scholar

  • Hughes, M., R. Appleton, C. Brooks, M. Hall, and C. Wyatt. 2006. “Notational Analysis of Elitemen’s Water-Polo.” Proceeding of 7th World Congress of Performance Analysis, Szombathely, Hungary 137–159.Google Scholar

  • Kubatko, J., D. Oliver, K. Pelto, and D. T. Rosenbaum. 2007. “A Starting Point for Analyzing Basketball Statistics.” Journal of Quantitative Analysis in Sports 3:516–525.Google Scholar

  • Lorenzo, A., A. Gomez, E. Ortega, S. Ibanez, and J. Sampaio. 2010. “Game Related Statistics which Discriminate between Winning and Losing in Under 16 Male Basketball Games.” Journal of Sports Science and Medicine 9:664–668.Google Scholar

  • Lozovina, V., L. Pavii, and M. Lozovina. 2004. “Analysis of Indicators of the Load During the Game in the Activity of the Centre in Waterpolo.” Nae More 135–141.Google Scholar

  • Lupo, C., G. Condello, L. Capranica, and A. Tessitore. 2014. “Women’s Water Polo World Championships: Technical and Tactical Aspects of Winning and Losing Teams in Close and Unbalanced Games.” Journal of Strength and Conditioning Research 28(1):210–222.Web of ScienceCrossrefGoogle Scholar

  • Lupo, C., G. Condello, and A. Tessitore. 2012a. “Notational Analysis of Elite Men’s Water Polo Related to Specific Margins of Victory.” Journal of Sports Science and Medicine 11: 516–525.Google Scholar

  • Lupo, C., C. Minganti, C. Cortis, F. Perroni, L. Capranica, and A. Tessitore. 2012b. “Effects of Competition Level on the Centre Forward Role of Men’s Water Polo.” Journal of Sports Sciences 30(9):889–897.CrossrefWeb of ScienceGoogle Scholar

  • Lupo, C., A. Tessitore, C. Cortis, A. Ammendolia, F. Figura, and L. Capranica. 2009. “A Physiological, Time-Motion, and Technical Comparison of Youth Water Polo and Acquagoal.” Journal of Sports Sciences 27(8):823–831.Web of ScienceCrossrefGoogle Scholar

  • Lupo, C., A. Tessitore, C. Minganti, and L. Capranica. 2010. “Notational Analysis of Elite and Sub-Elite Water Polo Matches.” Journal of Strength and Conditioning Research 24(1):223–229.CrossrefGoogle Scholar

  • Lupo, C., A. Tessitore, C. Minganti, B. King, C. Cortis, and L. Capranica. 2011. “Notational Analysis of American Women’s Collegiate Water Polo Matches.” Journal of Strength and Conditioning Research 25(3):753–757.Web of ScienceCrossrefGoogle Scholar

  • Platanou, T., G. Grasso, B. Cufino, and Y. Giannouris. 2007. “Comparison of the Offensive Action in Water Polo Games with the Old and New Rules.” Proceeding of the 12th European Collegiate of Sport Sciences, Jyvaskyla, Finland.Google Scholar

About the article

Corresponding author: John Mayberry, University of the Pacific – Mathematics, 3601 Pacific Ave., Stockton, California 95211, USA, Tel.: +209.946.3166, e-mail:


Published Online: 2014-02-21

Published in Print: 2014-01-01


In basketball, it is common to distinguish between plays and possessions (Kubatko et al. 2007), the latter referring to the period of game play between which a team gains control of the ball until the time at which control passes to the opposing team. In this paper, we look only at plays because (i) we are more interested in the outcome of specific tactical choices and (ii) the proportion of plays ending in non-possession ending outcomes such as corner or rebound is relatively small anyways.

We exclude exclusions resulting from exclusions in this calculation so that ε is technically the conditional probability that a power-play situation results in a goal given that the power-play resulted in a return to an even situation or counterattack.

Exceptions included penalty shots and shooting percentage for centre and direct shots, which all received values of 0 for both teams in about 40% of contests in our sample.

There was also one game in which both teams had the same ECR.

The correlation is similar if one looks just at perimeter shooting percentage.

We excluded all games against last place finishers Kazakhstan and Great Britain as well as the Serbia vs. Romania game because it occurred after Romania was eliminated from playoff contention.

Note that we round nijα to the nearest integer.

The two exceptions being USA vs Hungary and USA vs Montenegro.

In fact, one can compute the probability that team i beats j by k goals for any k≥0 in a similar manner.

Incorrect: Serbia vs Hungary and Montenegro vs Hungary. With α=0.03, we also incorrectly predicted Hungary vs USA so it appears that the Exclusion Model with low offensive weight does especially poorly in predicting matches involving Hungary.

... and USA vs Montenegro in the preliminary round.


Citation Information: Journal of Quantitative Analysis in Sports, ISSN (Online) 1559-0410, ISSN (Print) 2194-6388, DOI: https://doi.org/10.1515/jqas-2013-0127.

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[1]
James Graham and John Mayberry
Journal of Sports Analytics, 2016, Volume 2, Number 2, Page 61

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