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 MoreIn the previous post we saw that large or what might be called "blowout" victories in AFL games occurred at a rate much higher than the rate at which bookmakers assigned equally large handicaps to those same games, and we saw why this might be the case from a theoretical perspective on the assumption that AFL game margins were Normally distributed.
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 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 MoreIn the previous blog here on Statistical Analysis I referred to this paper and applied its drift-free Random Walk model to the "safety" of leads recent AFL history, finding that, to some extent, it fitted empirical data well.
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I came across an interesting journal article this week, published in March of 2015 on arXiv.org and entitled "Safe Leads and Lead Changes in Competitive Team Sports".
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 MoreThis 2015 season, seven rounds in, has felt like one where leads of any size have been less comfortable.
Read MoreIn the comments section of the previous blog, LT pointed out that Bookmakers seem to be doing a better job this year predicting the sum of the Home Team and Away Team scores than predicting the difference between them.
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 MoreMuch has already been written about the lamentable and historic-for-all-the-wrong-reasons 2014 Grand Final, which got me to wondering about exactly how atypical it was. Have there been similar Grand Finals and, if so, when?
Read MoreAlmost two years ago, in a post-GF funk, I recall painstakingly cutting-and-pasting the scoring progression from the afltables site for 100 randomly-selected games from 2012. I used that data to search for evidence of in-game momentum, there characterising it as the tendency for a team that's just scored to be the team that's more likely to score next.
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 MoreOne of the bets that's offered by TAB Sportsbet is on which of the teams will be the first to score 25 points. After analysing scoring event data for the period 2008 to 2014 provided by Paul from afltables.com I was surprised to discover that the first team to score 25 points goes on to win the game over 70% of the time.
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.
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