Today'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 MoreAs the FootyMaths Institute has highlighted in its most recent post, this year sees a startlingly wide spread of winning rates amongst the eight Finalists when we consider only their games from the home-and-away seasons against one another.
Read MoreThere's been a lot of discussion about the unusual situation of Fremantle, which finds itself Minor Premiers but only on the third line of the Flag wagering markets.
Read MoreYesterday's post led to an interesting Twitter thread last evening, which included a suggestion to reanalyse the data to determine whether price movements in the Pinnacle head-to-head market might have predictive value in other markets for the same game, specifically in the line market.
Read MoreWatching the TAB markets as they've shifted across the course of a week I've often wondered if there might be something predictive in those movements. If the eventual favourite's price has shortened during the week, does it win more or less often than its closing price would suggest?
Read MoreRecently, in light of the discussions about the validity of the season simulations written up over on the Simulations journal, I got to thinking about modelling the Bookmaker's price-setting behaviour and how it might be expected to respond to the outcomes of earlier games in the season. It's a topic I've investigated before, but not for a while.
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 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 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 MoreIn the last blog on this part of the site I introduced the MoSSBOD Team Rating System, the defining characteristics of which were that it Rated teams based on Scoring Shot Production and Concession and that it provided both a Defensive and an Offensive Rating for all teams.
Today I want to explore the history of those Ratings across the last decade to see what MoSSBOD has to say about the strongest and weakest Offensive and Defensive teams across that period.
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 MoreI'm a sucker for a colourful chart, and today's is based on simulations using an earlier model of Home and Away team scoring, constrained by bookmaker-based empirical realities.
Read MoreThis 2015 season, seven rounds in, has felt like one where leads of any size have been less comfortable.
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