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 MoreIt's been a while since we've reviewed the history of game margins and, in today's blog, we'll consider that history from a number of perspectives.
Read MoreToday's post continues the recent theme of entries here in the Statistical Analysis part of the site, taking yet another look at Scoring Shot Conversion rates but this time on a team-by-team and venue-by-venue basis.
Read MoreSubsequent to the previous post, which looked broadly at the differences in aggregate scoring metrics for Home-and-Away games compared to Finals from the same era, Friend of MoS, Liam, made a great suggestion for a follow up analysis.
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 MoreThree distinct pieces from three different Friends of MoS were the direct ingredients for this fresh (to MoS, anyway) take on team ratings
Read MoreIn recent blogs we've being exploring a range of topics related to team scoring, all of them based on a model I created in a series of blogs
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 MoreAccurate kicking, obviously, contributes to a team's success. But, just how much does it contribute, what are some of the sources of its variability, and just how predictable is it?
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 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 MoreMost sporting codes with a history of any significant length will eventually be described in terms of having passed through a number of eras, one or both ends of which are usually defined by some relatively obvious characteristic that forms the basis of the discussion.
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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