Round 6 Predictions

While I was hoping to have this site up and running before the season started, my PhD thesis and then full time work got in the way.

Nonetheless, its not too late to start posting predictions! I plan to maybe go back and revisit a priori how my model would have performed in the early rounds but that’s for a later time. You can at least check in on my pre-season rankings.

Here are my ELO predictions for Round 6. At some stage, I plan to turn this into an interactive page, similar to the FiveThirtyEight ones but for now, I’ll just post some predictions.

Probability and Margins for each game in round 6, 2016
Probability and Margins for each game in round 6, 2016

In future weeks I may do a game by game summary but for now, I’ll make some general observations.

  • There appear to be a bunch of close games this weekend, with NM v WB, Rich v PA and Carl v Ess all seemingly tough to pick.
  • I’ve written about Freo’s start to the year being so bad – things aren’t looking great for turning that around against an in form Adelaide
  • I’m not sure why my model is predicting such a big win for Hawthorn but it will be interesting to see how that one pans out

I should also note that I’m putting both my Margin and Probability predictions into the Monash tipping competition (full disclosure, there are no prizes). I’ll update on how they are going later on.

Annus horribilis Fremantle

By his own words, Ross Lyons team have are having an Annus Horribilis 1. It doesn’t take a whole lot of in depth data to know that starting the year with 0 wins and 5 losses is bad, and there is no shortage of stories describing just how bad that is.

The Arc shared a great visualisation looking at the progression of wins by minor premiers in the year following their minor premiership. Only Richmond in 1983 has started 0-5 from minor premiers, while the worst final season record is the Kangaroos in 1984, who won 5 games all year.

Where do we think Freo might get to? I thought I’d further explore the Dockers poor start by comparing to some other historical distributions to see just how bad it is. Below I’ve plotted the distribution of games won after 5 rounds, where we see, as we expect, a relatively normal distribution across those 5 games. Approximately 7.9% of teams, regardless of quality, begin a season at 0-5 in the history of the AFL (this drops to around 6% if we just look at 1990 to 2015).

1DistStart

Looking at how these groups of teams finished the season, we can see that early wins (i.e. games won after 5 rounds) seems pretty predictive of absolute final wins by season end. Of course, within those final wins is included the early wins, but removing them and showing ‘relative’ final wins (i.e. the record after round 5) shows a similar pattern.

RelAndMost

Focussing just on the top graph, where we have our 0-5 teams, it becomes apparent that very few teams who start 0-5 end up turning their season around. Below, I’ve picked out the best performing teams, in terms of final wins, after starting 0-5 2

05starters

The data show that only two teams have won 12 games,often discussed as the finals cutoff 3 after starting 0-5 – Richmond in 1924, who happened to win the final game of the Finals that year but didn’t win the premiership and Collingwood in 1959, interestingly the premiers from the year before.

So, against historical data, Freo has a pretty huge task ahead of them to make finals. This does however fail to take into account many factors. Maybe other teams who start 0-5 are just really really bad teams and Freo isn’t? Perhaps Freo has had a really tough start to the season in terms of opponents (they don’t appear to across the season) compared to the general 0-5 team?

Luckily, we can try to account for such things by using a rating system, such as my ELO ratings, which allows us to simulate expected results based upon the inherit skill and form of a particular team. Below, I’ve simulated a season 10000 times for the 1st 5 rounds and the rest of the season. The 1st 5 rounds are based on our pre-season rankings for Fremantle (we had them 9th) and their draw. We saw them start 0-5 around 3.7% of the time, below the historical average discussed above, and had them on average finishing with 2.7 wins on average.

FreoSim

From here on in (i.e. simulating their season from R6 onwards), we can see that, like historical teams that start 0-5, we don’t give Freo much chance of moving up the ladder. They do seem to fare slightly better than the average 0-5 time however and they do get at least 12 wins 8.7% of the time, which is higher than our historical 0-5 teams. Either our model has been too slow to adjust to Freo’s lack of ability or they aren’t your typical 0-5 team!

A lot of this assumes that Freo will continue to try and win as many games as possible, which may not be the case. Nonetheless, if anyone can turn a team around, and provide them with an Annus Mirabilis one might expect a coach like Ross Lyon, known for getting every inch out of his list, might be able to do it. At least our current rating of Freo (as the 10th best team) gives them a small chance. That is if Ross doesn’t jump ship early!

Notes:

  1. much to my dismay, that word doesn’t have the low brow meaning I had hoped – instead it means “horrible year”)
  2. In reviewing this graph, it revealed a slight error in my methodology as some teams such as North Melbourne in 2011 didn’t start at 0-5 but in fact had 0 wins after round 5, with a bye. I may try and fix this later on but I suspect it won’t change things a whole lot
  3. I sense a good blog post coming!

Start of 2016 season AFL ELO ratings

Throughout the year, I’m hoping to use my ELO ratings system to predict results and discuss various stories that emerge in the AFL season. I’ve described the model in a fair bit of detail elsewhere on the site but basically, an ELO rating system allows to compare the relative ‘skill’ of two teams and, using the difference in ratings, predict an outcome. It is nice due to its simplicity – the data it uses is the match margin and which team is at home, ignoring other information such as players, coaches, weather and so on.

Granted I’m writing this after round 5 and a fair bit has happened in the AFL already (such as Freo, Port, Collingwood and Richmond all sucking) but I wanted to have this post as a record of what my model thought at the start of the year. These ratings essentially are our end of year ratings for 2016, regressed toward the mean rating of 1500.

ELO_preR1

I may also update this post later with some simulations for the season but this is enough for now!

The First (last) Post!

Each year, as I watch the traditional Anzac day clash, I am often hit by an overwhelming sense of pointlessness in my chosen passion. There isn’t a greater example of why Sport really isn’t that important in the scheme of things than hosting a game on the day we remember fallen soldiers. As someone who consumes a lot of Sport, is employed in the industry and (as of this post) has started a blog exploring the vagaries of data in sport, I often battle with this feeling.

Missing Image
Collingwood and Essendon run through a shared banner on ANZAC day. Image from abc.net.au. Getty Images: Michael Dodge

On this same weekend, I read a great article in the NY Times, entitled What Happens When Baseball-Stats Nerds Run a Pro Team? This is a fascinating exploration into the world of Sabermetrics in America and in particular, when that starts to become to main focus of an organisation. While I firmly believe that using data to assist in various decisions in the sporting context is crucial to making, on average, the best decisions, it was really interesting to read the challenges faced when variables outside of the measured data set begin to influence the outcomes of their decisions.

The take home message form the article came in the idea that regardless of what the data point to, the most important part of the formula is the story. Players didn’t trust the “nerds” telling them that on, on average, they should throw pitch X over pitch Y under Z conditions. The players needed a story to focus on.

In the season’s final weeks, we changed course, focusing less on data and more on story. When we brought our left fielder in to reinforce our infield against a batter who hit only ground balls, we sold it as fun and adventurous. When we started using our closer in tight spots as early as the fifth inning — instead of the ninth, as every other team does — we kept our message as simple as could be: The game is on the line, so let’s take the bad pitcher out and put the good one in.

Towards to end of the article, they discuss the fact that a newly acquired pitcher, Sean Conroy, came out as the first openly gay Pro baseball player. In particular, they noted how they needed to learn from Controy about telling his story in the right way.

Conroy did what we failed to do. He didn’t make his story seem scary, and he didn’t make it about himself. He made it about the team. If we’d started our season thinking about the story we were telling, maybe we would have made history

This article to me showed me why sport can be important in society. Firstly, the importance of Sport in my life is because of the great stories that exist. From the sadness of the death of our sports stars, to exploring issues in the broader societal context such as gender inequality or racism, sport gives us an important outlet.

These stories transcend sport and provide, in my opinion, important discussions that are generally avoided. Even within the realm of the contest however, some of my best memories are watching unforgettable sporting moments such as amazing finale to the Australian Netball Championship,  my beloved Hawthorn winning three grand finals in a row or John Aloisi’s penalty to get Australia into the World Cup.

These are all stories that resonate and stick with me for a long time. In this blog, I’m hoping to explore my passion for such stories and use data to help explore these stories from a different perspective. If you go to my About Me page, you can see many of my inspirations for this blog, but a huge one for my is Nate Silvers FiveThiryEight and some of the fantastic infographics on this site.

Kobe Bryants shooting
An infographic around Kobe Bryants shooting efficiency. Image from FiveThityEight.com

I love the way the authors of this blog are able to utilise data to help tell a story. It’s not for everyone (mostly stubborn old players) but I think it certainly enhances some of the great story lines that exist. With this blog, I’m hoping to use data, and learn more about data science, to explore stories that interest me. In particular, I hope one day to expand this blog into a broader exploration of data science stories in Australia, but for now, I’ll start with Sport and see how I go!