In the previous blog we reviewed the Conversion Rate history of the VFL/AFL competition looking, in turn, at how it has varied across eras for different venues and for different teams. That blog provides some useful context for this one and you might find it helpful to review it before proceeding.
Read MoreThe off-season always seems a good time for adopting a more sweeping historical perspective in the analyses here on MatterOfStats. Today we're going to be reviewing Scoring Shot Conversion rates across the 119 seasons of the VFL/AFL from both a venue and a team perspective.
Read MoreEarlier this year in this blog, I introduced the MoSSBODS Team Rating System, an ELO-style system that provides separate estimates of each team's offensive and defensive abilities, as well as a combined estimate formed from their sum. That blog post describes the main motivations for a MoSSBODS-like approach, which I'll not repeat here.
Read MoreFinals, by their nature, tend to pit more-evenly matched teams against one another, on average, than do games from the home-and-away season. It seems reasonable, therefore, to hypothesise that margins will tend to be smaller in Finals than in the home-and-away season, but what other changes in scoring behaviour might we expect to see?
Read MoreOn Twitter today someone made the observation that teams' accuracy or conversion rate differentials seemed to have been strongly correlated with their final ladder position.
Read MoreIn today's post I'll review the performance of all the teams that have been assessed as favourites by the TAB in games played during the period 2006 to the end of Round 17 in 2015, excluding only those games where the TAB bookmaker installed equal-favourites.
Read MoreRecently, I noted, somewhat in passing in this piece on close game and blowouts, the decline in overall team scoring, a topic that's receiving not a little attention within the football community at the moment, fuelled partly by some recent low-scoring games, in particular the Dees v Lions encounter.
Read MoreThree distinct pieces from three different Friends of MoS were the direct ingredients for this fresh (to MoS, anyway) take on team ratings
Read MoreThe last few blogs here on the Statistical Analyses part of the website have used a model of team scoring that I fitted late last year to explore features of game scores and outcomes that we might expect to observe if that model is a reasonable approximation of reality.
Read MoreSo far this season, eight teams have lost after generating more scoring shots than their opponents and three more have been defeated despite matching their opponent's scoring shot production, which means that the outcome of over 15% of games might this year have been reversed had the losing team kicked straighter.
Read MoreIn my earlier posts on statistically modelling team scoring (see here and here) I treated Scoring Shot conversion as a phenomenon best represented by the Beta Binomial distribution and proceeded to empirically estimate the parameters for two such distributions, one to model the Home team conversion process and the other to model Away team conversion. The realised conversion rates for the Home team and for the Away team in any particular game were assumed to be random, independent draws from these two fixed distributions.
Read MoreSoccer goals, analysis suggests, are scored at different rates throughout the course of matches as teams tire and as, sometimes, one team is forced to press for a goal or chooses to concentrate on defending. Armed with the data provided by Paul from afltables.com, which includes every scoring and end-of-quarter event from every game played between the start of season 2008 and the end of the home-and-away season of 2014, we can investigate whether or not the same is true of AFL scoring.
Read MoreThe notion of momentum gets flung about in sports commentary as if it's some fundamental force, like gravity, that apparently acts at both long and short distances. Teams have - or don't have - momentum for periods as short as a few minutes, for perhaps half a quarter, going into the half-time break, entering the Finals, and sometimes even as they enter a new season, though I think when we start talking about momentum at the macro scale we wander perilously close to confusing it with another fundamental sporting force: form. It's a topic I've addressed, in its various forms, numerous times on MoS.
Read MoreIn a recent blog I developed an empirical model of AFL scoring in which I assumed that the Scoring Shots generated by Home and Away teams could be modelled by a bivariate Negative Binomial and that the conversion of these shots into Goals could be modelled by Beta Binomials.
Read MoreIn the previous blog on this topic I posited that the Scoring Shot production of a team could be modelled as a Poisson random variable with some predetermined mean, and that the conversion of these Scoring Shots into Goals could be modelled as a BetaBinomial with fixed conversion probability and theta (a spread parameter).
Read MoreThis week, thanks to Amazon, who replaced my unreadable Kindle copy of David W Miller's Fitting Frequency Distributions: Philosophy and Practice with a dead-tree version that could easily be used as a weapon such is its heft (and assuming you had the strength to wield it), I've been reminded of the importance of motivating my distributional choices with a plausible narrative. It's not good enough, he contends, to find that, say, a Gamma Distribution fits your data set really well, you should be able to explain why it's an appropriate choice from first principles.
Read MoreWe know that the TAB Bookmaker is exceptionally well-calibrated. Teams that he rates 80% chances win about 80% of the time and, more generally, teams that he rates X% chances win about X% of the time. Put another way, teams rated X% chances score more than their opponents X% of the time.
What about other scoring metrics, I wondered?
Read MoreIn response to my earlier post on the explained and unexplained portions of game margins, Friend of MatterOfStats, Michael, e-mailed me to suggest that variability in teams' points-scoring per scoring shot - or, equivalently, teams' conversion rates - might usefully be explored as a source of unexplained variability.
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