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.

Analyzing the prevalence of sports-related terms among the web sites of global corporations

This research investigates the prevalence of sports-related terms among the Web sites of the world’s leading companies, the Fortune Global 500. An automated process copied about four gigabytes of textual data, around 70 million words, from their sites. The subsequent analysis revealed regional and industry differences in the distribution of sports-related terms, the popularity of tennis stars and few references to sports stars, especially in Asia.

Characterizing multi-agent team behavior from partial team tracings : evidence from the English Premier League

Real-world AI systems have been recently deployed which can automatically analyze the plan and tactics of tennis players. As the game-state is updated regularly at short intervals (i.e. point-level), a library of successful and unsuccessful plans of a player can be learnt over time. Given the relative strengths and weaknesses of a player’s plans, a set of proven plans or tactics from the library that characterize a player can be identified. For low-scoring, continuous team sports like soccer, such analysis for multi-agent teams does not exist as the game is not segmented into “discretized” plays (i.e. plans), making it difficult to obtain a library that characterizes a team’s behavior. Additionally, as player tracking data is costly and difficult to obtain, we only have partial team tracings in the form of ball actions which makes this problem even more difficult. In this paper, we propose a method to overcome these issues by representing team behavior via play-segments, which are spatio-temporal descriptions of ball movement over fixed windows of time. Using these representations we can characterize team behavior from entropy maps, which give a measure of predictability of team behaviors across the field. We show the efficacy and applicability of our method on the 2010-2011 English Premier League soccer data.

Slicing big data - Twitter, gambling and time sensitive information

Big Data presents many challenges related to volume, whether one is interested in studying past datasets or, even more problematically, attempting to work with live streams of data. The most obvious challenge, in a ‘noisy’ environment such as contemporary social media, is to collect the pertinent information; be that information for a specific study, tweets which can inform emergency services or other responders to an ongoing crisis, or give an advantage to those involved in prediction markets. Often, such a process is iterative, with keywords and hashtags changing with the passage of time, and both collection and analytic methodologies need to be continually adapted to respond to this changing information. While many of the data sets collected and analyzed are preformed, that is they are built around a particular keyword, hashtag, or set of authors, they still contain a large volume of information, much of which is unnecessary for the current purpose and/or potentially useful for future projects. Accordingly, this panel considers methods for separating and combining data to optimize big data research and report findings to stakeholders. The first paper considers possible coding mechanisms for incoming tweets during a crisis, taking a large stream of incoming tweets and selecting which of those need to be immediately placed in front of responders, for manual filtering and possible action. The paper suggests two solutions for this, content analysis and user profiling. In the former case, aspects of the tweet are assigned a score to assess its likely relationship to the topic at hand, and the urgency of the information, whilst the latter attempts to identify those users who are either serving as amplifiers of information or are known as an authoritative source. Through these techniques, the information contained in a large dataset could be filtered down to match the expected capacity of emergency responders, and knowledge as to the core keywords or hashtags relating to the current event is constantly refined for future data collection. The second paper is also concerned with identifying significant tweets, but in this case tweets relevant to particular prediction market; tennis betting. As increasing numbers of professional sports men and women create Twitter accounts to communicate with their fans, information is being shared regarding injuries, form and emotions which have the potential to impact on future results. As has already been demonstrated with leading US sports, such information is extremely valuable. Tennis, as with American Football (NFL) and Baseball (MLB) has paid subscription services which manually filter incoming news sources, including tweets, for information valuable to gamblers, gambling operators, and fantasy sports players. However, whilst such services are still niche operations, much of the value of information is lost by the time it reaches one of these services. The paper thus considers how information could be filtered from twitter user lists and hash tag or keyword monitoring, assessing the value of the source, information, and the prediction markets to which it may relate. The third paper examines methods for collecting Twitter data and following changes in an ongoing, dynamic social movement, such as the Occupy Wall Street movement. It involves the development of technical infrastructure to collect and make the tweets available for exploration and analysis. A strategy to respond to changes in the social movement is also required or the resulting tweets will only reflect the discussions and strategies the movement used at the time the keyword list is created — in a way, keyword creation is part strategy and part art. In this paper we describe strategies for the creation of a social media archive, specifically tweets related to the Occupy Wall Street movement, and methods for continuing to adapt data collection strategies as the movement’s presence in Twitter changes over time. We also discuss the opportunities and methods to extract data smaller slices of data from an archive of social media data to support a multitude of research projects in multiple fields of study. The common theme amongst these papers is that of constructing a data set, filtering it for a specific purpose, and then using the resulting information to aid in future data collection. The intention is that through the papers presented, and subsequent discussion, the panel will inform the wider research community not only on the objectives and limitations of data collection, live analytics, and filtering, but also on current and in-development methodologies that could be adopted by those working with such datasets, and how such approaches could be customized depending on the project stakeholders.

Aha! The return of Alan Partridge

Throughout a long and occasionally distinguished career first as a television sports correspondent, then chat show host (dramatically ended by the accidental homicide of a guest live on air), then rebirth as a radio presenter at North Norfolk Digital, Alan Partridge has navigated the stormy waters of the British media landscape, now achieving mainstream success on the big screen with a starring role in Steve Coogan’s Alpha Papa (Declan Lowney, 2013). A man who in his desperation for a television series of his own once sank so low as to pitch a show called Monkey Tennis to the BBC finally finds his inner hero in a film which, while presenting mainly as comedy, also contains a biting critique of trends in the British media with which all journalists and media practitioners in general will be familiar. Alpha Papa is a nostalgic, elegiac riff on the pleasures and values of local radio the way it used to be, exemplified by North Norfolk Digital’s stable of flawed, but endearing jocks – Wally Banter, Bruno Brooks, Dave Clifton (who in one scene recounts the depths to which he sank as an alcoholic, drug addicted wreck—“I woke up in a skip with someone else’s underpants in my mouth. I can laugh about it now …”), and Pat Farrell. 50- something Pat is sacked by the new owners of North Norfolk Digital, who in their efforts to transform the station into a “multiplatform content provider” going by the more Gen Yfriendly name of Shape (“the way you want it to be”), wish to replace him with a younger, brattish model lacking in taste and manners. Out go records by the likes of Glen Campbell and Neil Diamond (“You can keep Jesus Christ”, observes Partridge after playing Diamond’s Sweet Caroline in a demonstration of the crackling radio repartee for which he is by now renowned, “that was the king of the Jews”), in comes Roachford. Pat, grieving his dead wife Molly, finally snaps and turns the glitzy media launch of Shape into a hostage siege. Only Alan Partridge, it seems, can step in and talk Pat out of a looming catastrophe.

Learn to love thy neighbour

A travel article about touring in New Zealand. ‘What’s the best thing about England?’ asked the Englishman next to me, quite suddenly, as we came out of a tunnel. ‘Well?’ I didn’t know. ‘Answer,’ he said, ‘is France.’ Here was the thing. My companion was a tennis pro, and these days he divided his time between London and Paris. Because you could. ‘Nothing big, which suits me: I am only moderately successful. Mainly rich ladies, if you know what I mean. Much prefer the French side, if you know what I mean.’ I think I knew what he meant...

Yeerongpilly TOD Master Plan

Urban Design Master Plan for Government site adjacent to the State Tennis Centre and Yeerongpilly Station.