I do have some reservations on the low standard deviations produced by the model – the random sampling methodology currently used is flawed and still very much under construction. I don’t have this data either. If you need help with other stats, I would be happy to help. I am looking for a simple spreadsheet to track stats during the game. There is no perceptible skewness in the distribution. Like Quote Reply.

The first was also a prime concern of mine when doing this. Also note that intercepts are the consequence of a turnovers. Yes/No. By 4:35pm when the Suns took on the Lions, the ground was seemingly dry and there was no report of wet/slippery weather and scoring certainly wasn’t affected. The order is based on the mean ladder position from 10,000 season simulations. In Round 5, 2015, the Gold Coast local weather station recorded a daily rainfall aggregate of 132mm after 9am. Each team itself gets a rating in 6/7 SOLDIER categories based on team form. I’m hanging on to second place in my table since I’ve gone “live”… assuming bits are more important , Environmental factors affecting AFL outcomes – the weather, part 2, http://www.afl.com.au/match-centre/2018/17/adel-v-geel, Environmental factors affecting AFL outcomes – the weather, previous piece that began to explore elements of home ground advantage (HGA), Goals, Goal Assists, Points/I50, Marks I50, Contested Possessions, Clearances, Tackles. What is definitely clear is the AFL Lab model does things a little differently. Is it possible that each venue has its own HGA independent of other factors?

It is by no means complete, professional and optimised and never will be. Either rain has little effect on the total points scored, or these daily rainfall figures do not represent the conditions at the football ground during the match. The keywords I chose (i.e. Maybe parsing match reports for keywords could work!

There are a lot of inefficient disposals in the wet, a lot of dropped marks and a lot of stoppages. In example, http://www.afl.com.au/match-centre/2018/17/adel-v-geel is from 2018, Round 17, is an Adelaide home game against Geelong. How well does this team form carry over to a new season? How do we quantify this in a meaningful way so that it may be used in a model? simultaneously.

The lasting effect of rain, perhaps after it stops, and other effects such as dew also causes these impacts perhaps to a lesser extent. I would argue the numbers should be largely independent of the weather; the efficiency will be the main difference. Wind comes in a couple of flavours. The SVR2 model predicted a median margin of -25 points (away team’s favour). Over round 17, when watching games/highlights I kept some notes about the conditions. : AFL Rising Star for Round 18 JavaScript is disabled. I would also think that player positioning would play a key role.

Without doing any of the quantitative measurements, it’s easy to argue why this is going to at least be very difficult.

Let’s start with something that shouldn’t be affected too much as a bit of a control measure. I am new at this Message Board stuff. Prevailing winds down the length of the ground provide a bias towards scoring at one end of the ground (i.e. The consequence is that the distributions plotted above are most likely biased. Nevertheless, I have some better ideas of how to proceed with this difficult problem. Legend G Games MIN Minutes PTS Points GLS Goals BH Behinds D Disposals HB Handballs K Kicks The chosen players for each team have a small but noticeable effect on the predicted outcome of the game. Wow, are Geelong that good? To protect you from spammers, the MrExcel board moderators will probably have removed it for you by the time you get this, but keep it in mind for future posts. Today I continue my focus on the weather. The game-prediction model uses recent (5-game) and longer-term (20-game) form of player and team performances to predict an outcome using the players selected on the team sheets. In total, there are fourteen input variables that go in to the model to produce the single output of the game margin. For this data (360 games), the mean is -0.825 and the median margin is -1. The standard deviations the model produces are generally between 18 and 50 points, and mostly on the lower end. Without reviewing every decision and classifying each as a “justified” free kick or an “umpiring error”, it is not possible to comment on favouritism as a concept. Not a brilliant week with the Melbourne tip but I was happy with it at the time. Wet weather games are anecdotally characterised by “scrappy football”; less handballing, more kicking, and low scores. These key statistics are selected from the data and assigned one of seven categories (SOLDIER); and each player in each past game are given a rating in these seven areas.

Having players in the right zones; close to both pick up loose balls out of a contest and ~60 metres back to intercept long bombs forward seems like the way to go. Having said that, assuming the player and team performances are projected as well as possible over a whole season, will the model’s prediction be accurate when the effect of rule changes is unknown? As discussed in the previous piece, the environmental factors consist of of many measurable and immeasurable modifiers that affect the outcome of a game. In particular, I will look at some key statistics that differentiate dry-weather football from wet-weather football. If there was mention of wind (or inferred through description of “sideways rain”, etc.) Perhaps this can be explained by teams not respecting slightly difficult conditions and trying to play a normal game style. These team statistics are not attributable to particular players and could be considered a descriptor of an overall game plan, or just team performance. From this measure, a model with each of the relevant HGA “predictors” identified could be matched. The reason is easy to explain.

The proportion of realisations where the home team wins represents the home win probability.


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