Proceedings of Mathsport international 2017 conference

Proceedings of MathSport International 2017 Conference, held in the Botanical Garden of the University of Padua, June 26-28, 2017. MathSport International organizes biennial conferences dedicated to all topics where mathematics and sport meet. Topics include: performance measures, optimization of sports performance, statistics and probability models, mathematical and physical models in sports, competitive strategies, statistics and probability match outcome models, optimal tournament design and scheduling, decision support systems, analysis of rules and adjudication, econometrics in sport, analysis of sporting technologies, financial valuation in sport, e-sports (gaming), betting and sports

Key Players and Key Groups in Teams

Using data for 2008-2009, we determine winning strategies for the game in two different formats: 50-over one-day internationals and 20-over games from the Indian Premier League and Twenty20 Internationals. We find that attacking batting and defensive bowling outperform all other strate- gies in determining the probability of winning in both formats despite the thirty over difference between them. Moreover, in both versions of the game, good elding turns out to be an important complement to these two strategies. We speculate that this will have implications for the future of cricket, especially for the popularity of formats and the composition of teams.

A Method for Inferring Batting Conditions in ODI Cricket from Historical Data

This paper is part of a wider research programme using a dynamic-programming approach to modelling the choices about the amount of risk to take by batting and bowling teams in One Day International cricket. An important confounding variable in this analysis is the ground conditions (size of ground, nature of pitch and weather conditions) that affect how many runs can be scored for a given amount of risk. This variable does not exist in our historical data set and would regardless be very difficult to accurately observe on the day of a match. In this paper, we consider a way of estimating a distribution for the ground conditions using only the information contained in the first-innings score and the result of the match. The approach uses this information to estimate the importance of ground conditions in the determination of first innings total scores. We assume a functional form for a model of first innings scores and we estimate the parameters of our model using Monte Carlo methods. We test the impact of a significant rule change and we apply our findings to selected matches before and after the new rules came into play.ODI Cricket; Batting Conditions; Nuisance Variable

Predicting the Winning Percentage of Limited-Overs Cricket Using the Pythagorean Formula

The Pythagorean Win-Loss formula can be effectively used to estimate winning percentages for sporting events. This formula was initially developed by baseball statistician Bill James and later was extended by other researchers to sports such as football, basketball, and ice hockey. Although one can calculate actual winning percentages based on the outcomes of played games, that approach does not take into account the margin of victory. The key benefit of the Pythagorean formula is its utilization of actual average runs scored and actual average runs allowed. This article presents the application of the Pythagorean Win-Loss formula to two different types of limited-overs cricket formats, namely One Day International cricket (ODI) and Twenty20 cricket. The data for the application was used from the matches played by the top 10 International Cricket Council (ICC) members who participated in the 2019 ICC Cricket World Cup. For matches for which the second batting team won, runs scored were estimated by considering the remaining amount of resources, based on the Duckworth-Lewis method

On the distribution of runs scored and batting strategy in test cricket

Negative binomial distributions are fitted to partnership scores and innings scores in test cricket. For partnership scores, we use a parametric model that allows us to consider run rate as a covariate in the distribution of runs scored and hence to use run rate as a surrogate for batting strategy. Then we describe the implied influence of run rate on match outcome probabilities given the state of the match at some point during the third innings; we refer to such a point in the match as the current position. Match outcome probabilities are calculated by using a model for the outcome given the end of the third-innings position, and a model for transitions from the current position to the end of the third-innings position, with transition probabilities considered as a function of run rate. Although the run rate is not wholly in the control of the batting side, our approach at least allows a captain or team analyst to consider the match outcome probability if the team can bat towards a target at a particular run rate. This will then at least indicate whether an aggressive or defensive batting strategy is desirable

A Probabilistic Approach to Identifying Run Scoring Advantage in the Order of Playing Cricket

In the game of cricket, the result of coin toss is assumed to be one of the determinants of match outcome. The decision to bat first after winning the toss is often taken to make the best use of superior pitch conditions and set a big target for the opponent. However, the opponent may fail to show their natural batting performance in the second innings due to a number of factors, including deteriorated pitch conditions and excessive pressure of chasing a high target score. The advantage of batting first has been highlighted in the literature and expert opinions, however, the effect of batting and bowling order on match outcome has not been investigated well enough to recommend a solution to any potential bias. This study proposes a probability theory-based model to study venue-specific scoring and chasing characteristics of teams under different match outcomes. A total of 1117 one-day international matches held in ten popular venues are analyzed to show substantially high scoring advantage and likelihood when the winning team bat in the first innings. Results suggest that the same 'bat-first' winning team is very unlikely to score or chase such a high score if they were to bat in the second innings. Therefore, the coin toss decision may favor one team over the other. A Bayesian model is proposed to revise the target score for each venue such that the winning and scoring likelihood is equal regardless of the toss decision. The data and source codes have been shared publicly for future research in creating competitive match outcomes by eliminating the advantage of batting order in run scoring

Tactics for Twenty20 cricket

This paper explores two avenues for the modification of tactics in Twenty20 cricket. The first idea is based on the realisation that wickets are of less importance in Twenty20 cricket than in other formats of cricket (e.g. one-day cricket and Test cricket). A consequence is that batting sides in Twenty20 cricket should place more emphasis on scoring runs and less emphasis on avoiding wickets falling. On the flip side, fielding sides should place more emphasis on preventing runs and less emphasis on taking wickets. Practical implementations of this general idea are obtained by simple modifications to batting orders and bowling overs. The second idea may be applicable when there exists a sizeable mismatch between two competing teams. In this case, the weaker team may be able to improve its win probability by increasing the variance of run differential. A specific variance inflation technique which we consider is increased aggressiveness in batting