Strategic Evaluation in AFL

©Journal of Sports Science and Medicine (2006) 5, 533-540 http://www.jssm.org Research article The 8th Australasian Conference on Mathematics and Computers in Sport, 3-5 July 2006, Queensland, Australia POSSESSION VERSUS POSITION: STRATEGIC EVALUATION IN AFL Darren M. O’Shaughnessy Champion Data, Melbourne, Australia Published (online): 15 December 2006 ABSTRACT In sports like Australian Rules football and soccer, teams must battle to achieve possession of the ball in sufficient space to make optimal use of it. Ultimately the teams need to score, and to do that the ball must be brought into the area in front of goal – the place where the defence usually concentrates on shutting down space and opportunity time. Coaches would like to quantify the trade-offs between contested play in good positions and uncontested play in less promising positions, in order to inform their decision- making about where to put their players, and when to gamble on sending the ball to a contest rather than simply maintain possession. To evaluate football strategies, Champion Data has collected the on-ground locations of all 350,000 possessions and stoppages in the past two seasons of AFL (2004, 2005). By following each chain of play through to the next score, we can now reliably estimate the scoreboard “equity” of possessing the ball at any location, and measure the effect of having sufficient time to dispose of it effectively. As expected, winning the ball under physical pressure (through a “hard ball get”) is far more difficult to convert into a score than winning it via a mark. We also analyse some equity gradients to show how getting the ball 20 metres closer to goal is much more important in certain areas of the ground than in others. We conclude by looking at the choices faced by players in possession wanting to maximise their likelihood of success. KEY WORDS: Notational analysis, Australian Rules Football, tactical coaching. INTRODUCTION of AFL and presents empirical interpretation of data from the 2004 and 2005 seasons. Australian Rules Football (informally known as AFL coaches are clamouring for this sort of “AFL” after the Australian Football League) is analysis to inform their strategies and training played with an oval ball on an oval field at high procedures. They know that being in possession of speed, leading to it sometimes being called “What the ball is important, but this research can show Rules?” by the unschooled observer. Compared to exactly how much it’s worth on the scoreboard to more structured football codes such as American take a contested mark, compared with someone football or rugby league where a “phase of play” from the opposition grabbing the loose ball spilled always starts in a simply-defined formation, the from the pack. They also know that position is free-flowing nature of Australian football creates important. They must create opportunities in extra dimensions for analysis. This paper describes positions near goal, but their players often have to the qualitative framework for evaluating the phases choose whether to aim at a riskier proposition close 534 Strategic evaluation in AFL to the goalmouth or maintain possession in a worse As noted in Equation 1, Match Equity is a function position. Dynamic programming based on of four parameters: empirically derived parameters can answer this dilemma. the score margin, m Dynamic programming was first applied to the time remaining in the match, t AFL (Clarke and Norman, 1998) to answer the the position on the field, x question of whether players should concede a point the possession state or phase of play, ϕ on the scoreboard in order to gain clean possession afterwards. A new thesis (Forbes, 2006) based on AFL typically has about 50 scores in a match Champion Data’s statistics uses a Markov model of 80 live minutes. We define styp as the typical approach to map out the probabilities of transitions score of a game (in AFL’s case, the goal worth 6 between AFL’s phases to predict scoreboard results. points is dominant), and ttyp as the typical time American football, where position is between scores (approximately 100 seconds in effectively one-dimensional and there are only four AFL). We can roughly decouple the first two phases – the “downs” – has been analysed using parameters from the others by noting that if we dynamic programming in a famous paper (Romer, discard any knowledge of x or ϕ , we can build a 2002), and a rating system (Schatz, 2005) called satisfactory model of winning probability based DVOA (Defence-adjusted Value Over Average) only on the time remaining t and changes to the evaluates actions with respect to a model of margin m. The phase and location information can scoreboard value similar to the one created in this be treated as a perturbation of the match-winning paper. The fast-flowing and open sport of ice probability model EM. hockey has recently been modelled using a “semi- To model the net potential value on the Markov” approach (Thomas, 2006). scoreboard of the current state of play, we introduce The modelling undertaken here is largely exploratory – this is a mass of new data which Field Equity: requires further detailed research. EF (x,ϕ) = ∑( pi,team si − pi,opp si ) METHODS i (2) − max(si ) ≤ EF ≤ max(si ) Match equity and field equity th Various authors have employed a plethora of terms where; si is the value of the i type of score pi,q is the to describe the expected value of actions on sporting probability of the next score being of type i by team q fields. Studeman (2004) describes the repeated reïnvention and relabelling of “Win Probability The Field Equities of each team in the contest Added” in baseball. Bennett (2005) has a good always sum to zero. The Field Equity fluctuates as simple description of how to value an action that play progresses until either team scores, at which alters the probability of winning the match. team it precipitates an actual change to the margin The terminology we use in this paper is m and EF is reset to zero. AFL has two different derived from the theory of backgammon (Keith, restart phases, one being a centre bounce after a 1996), a game in which the players compete to win goal (where obviously each team has equal chances points, the first to n points winning the match. We and EF = 0), the other being a kick-in from the assume teams of equal strength, although much of goalmouth after a behind. Remarkably, empirical the reasoning below is still valid for uneven teams. evidence suggests that the average team has zero Match Equity is the probability of the team to win residual equity in the behind restart phase (see the match from this moment, or more specifically: Table 2 in the Results section below). 1 Changes to Match Equity, Decoupled EM (m,t, x,ϕ) = pwin + pdraw ∆E ≈ Π(m,t) ⋅ ∆E (x,ϕ) 2 (1) M F ∂E (3) 0 ≤ EM ≤ 1 Π = M ∂m The Match Equities of each team in the contest sum to one. A team is always aiming to The “Pressure Factor” multiplier Π is the increase its Match Equity until it reaches one – impact an instantaneous change to the margin would certain victory. I.e., it is looking for actions which have on the match-winning chances of the teams. maximise ∆EM, or at the very least have ∆EM ≥ 0. Empirically, kicking the first goal in an evenly- O’Shaughnessy 535 matched contest increases EM from 0.50 to about AFL’s Phases. Phases of Play with a team in 0.56. The decoupling transfers the potential held in possession include: the field position into improved match-winning Mark. The player has caught the ball from a probability. It allows us to assume that a team that kick and according to the rules is entitled to increases EF to +2 soon after the start of a game consider his options without being tackled. increases its match-winning probability to about Handball Receive. The player has received a 0.52, but if only a quarter of the match is left and m handball from a teammate, uncontested. = 0, ∆EF of +2 could imply ∆EM of +0.04, from 0.50 Loose Ball Get. The ball has indiscriminately to 0.54. A detailed formula for Π is beyond the spilled loose and a player has been in the right place scope of this paper. Henceforth the term “equity” to pick it up. (E) will refer to Field Equity and we will assume Hard Ball Get. The player has taken usable the time remaining is effectively unlimited. possession of the football while under direct The decoupling assumption only breaks down physical pressure from an opponent. when both t and m are of the order of ttyp and styp Play can also be in an active neutral phase, respectively – i.e., when the game goes down to the after a smother of the ball or a similar random wire, the added quantum of a major score could be collision. There are also passive neutral phases the difference between a win (EM = 1) and a loss where the umpire holds the ball, before launching it (EM = 0), and the time left on the clock must be back into play. Lastly there are a couple of set-play considered. phases such as a kick-in after a behind. For the purposes of this paper we will Data collection consider five Phases of Play, which experience and Champion Data has been logging qualitative AFL analysis have shown cover most important facets of statistics by computer since 1996.

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