# Scoring Shot Conversion History in the VFL/AFL (1897-2015)

The 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.

# Team-by-Team and Venue-by-Venue Conversion Rate History

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.

Finals, 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?

In 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.

# Team Scoring Model Parameter Sensitivity

In 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

# A Few More Simulations: Losing With More Scoring Shots and Playing a Draw

The 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.

# The Importance of Goal-Kicking Accuracy

So 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.

This 2015 season, seven rounds in, has felt like one where leads of any size have been less comfortable.

# Predicting Total Game Scores Versus Predicting Margins

In 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.

# Kicking Accuracy : Teams, Rounds and Correlations

Accurate 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?

# Fuzzy Clustering of VFL/AFL Grand Final Scores

Much 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?

# Scoring Catenation: An Alternative Measure of Momentum

Almost 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.

# Who Scores Next? Scoring Event Patterns in AFL

Today another dive into the scoring event data provided by Paul over at afltables, specifically to look at the patterns in successive Scoring Events. How likely, for example, are we to see a Home team Goal as the next Scoring Event after another Home team Goal?

# Scoring Shot Conversion Rates: How Predictable Are They?

In 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.