Physiological, perceptual and technical responses to on-court tennis training on hard and clay courts

The aim of this study was to investigate the effect of court surface (clay v hard-court) on technical, physiological and perceptual responses to on-court training. Four high-performance junior male players performed two identical training sessions on hard and clay courts, respectively. Sessions included both physical conditioning and technical elements as led by the coach. Each session was filmed for later notational analysis of stroke count and error rates. Further, players wore a global positioning satellite device to measure distance covered during each session; whilst heart rate, countermovement jump distance and capillary blood measures of metabolites were measured before, during and following each session. Additionally a respective coach and athlete rating of perceived exertion (RPE) were measured following each session. Total duration and distance covered during of each session were comparable (P>0.05; d<0.20). While forehand and backhands stroke volume did not differ between sessions (P>0.05; d<0.30); large effects for increased unforced and forced errors were present on the hard court (P>0.05; d>0.90). Furthermore, large effects for increased heart rate, blood lactate and RPE values were evident on clay compared to hard courts (P>0.05; d>0.90). Additionally, while player and coach RPE on hard courts were similar, there were large effects for coaches to underrate the RPE of players on clay courts (P>0.05; d>0.90). In conclusion, training on clay courts results in trends for increased heart rate, lactate and RPE values, suggesting sessions on clay tend towards higher physiological and perceptual loads than hard courts. Further, coaches appear effective at rating player RPE on hard courts, but may underrate the perceived exertion of sessions on clay courts.

The momentum effect in competitions : field evidence from tennis matches

It is often suggested that there is a psychological advantage to be leading in a competition. It is, however, hard to identify such an effect econometrically. Using a Regression Discontinuity Design over a large dataset of tennis matches (N=634,095) the present paper exploits the randomised variation in first set results that occurs when the first set is decided by a close tie break (N=72,294). I find that winning the first set has a significant and strong effect on the result of the second set. A player who wins a close first set tie break will, on average, win one game more in the second set. I discuss the likely economic and psychological explanations of this phenomenon.

Predicting shot locations in tennis using spatiotemporal data

Over the past decade, vision-based tracking systems have been successfully deployed in professional sports such as tennis and cricket for enhanced broadcast visualizations as well as aiding umpiring decisions. Despite the high-level of accuracy of the tracking systems and the sheer volume of spatiotemporal data they generate, the use of this high quality data for quantitative player performance and prediction has been lacking. In this paper, we present a method which predicts the location of a future shot based on the spatiotemporal parameters of the incoming shots (i.e. shot speed, location, angle and feet location) from such a vision system. Having the ability to accurately predict future short-term events has enormous implications in the area of automatic sports broadcasting in addition to coaching and commentary domains. Using Hawk-Eye data from the 2012 Australian Open Men's draw, we utilize a Dynamic Bayesian Network to model player behaviors and use an online model adaptation method to match the player's behavior to enhance shot predictability. To show the utility of our approach, we analyze the shot predictability of the top 3 players seeds in the tournament (Djokovic, Federer and Nadal) as they played the most amounts of games.

Analyzing and predicting events in soccer and tennis using spatiotemporal data

In this paper, we summarize our recent work in analyz- ing and predicting behaviors in sports using spatiotemporal data. We specifically focus on two recent works: 1) Predicting the location of shot in tennis using Hawk-Eye tennis data, and 2) Clustering spatiotemporal plays in soccer to discover the methods in which they get a shot on goal from a professional league.

Forecasting outcomes in tennis matches using within-match betting markets

Klaassen and Magnus (2003) provide a model of the probability of a given player winning a tennis match, with the prediction updated on a point-by-point basis. This paper provides a point-by-point comparison of that model with the probability of a given player winning the match, as implied by betting odds. The predictions implied by the betting odds match the model predictions closely, with an extremely high correlation being found between the model and the betting market. The results for both men’s and women’s matches also suggest that there is a high level of efficiency in the betting market, demonstrating that betting markets are a good predictor of the outcomes of tennis matches. The significance of service breaks and service being held is anticipated up to four points prior to the end of the game. However, the tendency of players to lose more points than would be expected after conceding a break of service is not captured instantaneously in betting odds. In contrast, there is no evidence of a biased reaction to a player winning a game on service.