In sport, some events, occurrences, and outcomes are more probable than others, and the potential exists to use information about probabilities to aid performance. This entry discusses two levels on which probabilities are relevant to sport performance. The first is the individual, immediate performance level. On this level, probabilities are used implicitly during performance; performers may not even be aware that they are using probabilities, having built up this feature of anticipation and decision making (DM) over time as they acquired skill. The second level is a broader, game-analysis and planning level, on which information on probabilities is actively sought to drive strategic decisions. This second level seeks to explicitly exploit probabilities for planning and competitive advantage. It uses a more comprehensive approach, often accessing large pools of data to identify meaningful patterns that exist on the individual, implicit level. Thus a strong link exists between the two levels of probabilities, with one influencing the other, as researchers use probability information to drive interventions and strategy. The sections that follow discuss the individual and the tactical planning levels of probabilities by presenting examples that illustrate how probabilities are used in sport and exercise psychology (SEP) research.
Individual Level
Though there is relatively little direct exploration of the explicit use of probabilities in DM for individual action choices, it is well known that performers make informal “calculations” and use probabilities based on the situation. In competition, athletes implicitly assign weightings, or probabilities, to the likelihood of an event occurring and use this information to guide their decisions. These implicit, subjective probabilities influence the ability to respond. For instance, a tennis player’s position on the court can influence the type of shot he or she is able to play. Knowing this, a defender can assign probabilities to each possible shot to anticipate the opponent’s actions. If a defender thinks the probability of a particular shot is high, the defender will show better anticipation of this shot. A shot the defender thinks is less probable will not be as well anticipated if the opponent makes this choice. The attacking player can exploit this information by playing a low-probability shot when another outcome is highly probable and thus catch the defender ill-prepared or off guard.
The effective use of probabilities can also increase performance based on availability of specific situational information. This has been explored by manipulating the situational information accompanying video clips when performers are asked to decide on their next action. For example, a baseball pitcher is more likely to try to throw a strike pitch rather than a pitch outside the strike zone when the pitch count contains three balls than when it contains none. This situational information allows a batter to make an informed decision regarding the pitch location and, by reducing uncertainty, make a quicker decision about whether or not to swing.
The use of probabilities also distinguishes between more and less skilled performers. The use of verbal reports, in which athletes report their thoughts during play, have shown that elite athletes, compared with less skilled athletes, are able to create more detailed and sophisticated event profiles using past and current information to “diagnose” what is currently taking place, as well as predict and anticipate future play. This probabilistic information can be used to effectively manage energy expenditure during the course of an event. In squash, for instance, the probability of winning a game is based on the probability of winning a rally and the relative importance of each rally. Athletes naturally use this information to regulate energy expended on a particular point and energy trade-offs in the context of competition as a whole and its likely outcome.
Relying on probabilities is not always beneficial, however; athletes who believe in the “hot hand” in basketball, for instance, think there is a greater probability of a player making a successful shot after they have made one or two previous shots. Teammates are thus more likely to allocate or pass the ball to a “hot” player even though the evidence to support the hot-hand belief is equivocal. Sports officials are also prone to using situational probabilities when making decisions; they are more likely to penalize teams in an alternating fashion rather than penalizing the same team twice in a row. Cognitive biases and decision errors such as these provide a place for the use of probabilities in sport on a more tactical level.
Tactical Planning Level
Coaches and athletes often use intuitive methods to derive tactics. Work in the area of calculation, statistics, and machine learning methods are used to support these intuitive processes and avoid errors. Using archived or accumulated performance data, data analytical methods focus on maximizing performance by more fully exploiting information for tactical planning and are especially useful in sports, such as rowing and cycling, where pacing is critical. For example, historical performance data can be used to both create race profiles that typify competitors from one team or country and to determine optimal race patterns in sports like rowing, which have different race portions (e.g., 500-m splits of a 2,000-m race). This information is used to improve planning and the chances of achieving success.
The tactical, planning level of probabilities is also useful in multievent sports, where the final standings are determined after more than one event (e.g., triathlon, decathlon). In some multievent sports, such as the cycling omnium, pacing is considered between events over more than 1 day. The omnium is comprised of six track cycling events that take place over 2 days. The introduction of a new event (the elimination race) to create a six-event omnium, before the 2012 Olympics, is a case where the tactical, planning level of probabilities is useful to investigate the best strategy to secure a medal. Machine learning analyses have been used to identify the minimum result required in each event that will lead to the highest probability of success, as well as whether the addition of the new elimination race event benefits endurance riders or sprinters. Not only does this use of probabilities help planning before and during the event (i.e., between events, between days) but it can also be useful information for athlete selection.
In general, SEP seeks to increase overall probabilities of successful performance. Individual DM “in the moment” and structured methodical planning based on the likelihoods of different events are influential in reaching this goal.
References:
- Paull, G., & Glencross, D. (1997). Expert perception and decision making in baseball. International Journal of Sport Psychology, 28, 35–56.
- Alain, C., & Proteau, L. (1980). Decision making in sport. In C. H. Nadeau, W. R. Halliwell, K. M. Newell, & G. C. Roberts (Eds.), Psychology of motor behavior and sport—1979 (pp. 465–477). Champaign, IL: Human Kinetics.
- Ofoghi, B., Zeleznikow, J., & MacMahon, C. (2011). Probabilistic modelling to give advice about rowing split measures to support strategy and pacing in race planning. International Journal of Performance Analysis in Sport, 11(2), 239–253.
- Ofoghi, B., Zeleznikow, J., MacMahon, C., & Dwyer, D. (2011). Has the addition of the elimination race to the track cycling omnium benefitted sprinters or endurance riders? Proceedings of the International Symposium on Computer Science in Sport 2011, Shanghai, China, pp. 34–37.
See also:
- Sports Psychology
- Perception in Sport