When the bookies strongly back a team to win.. and they don’t… well that’s an upset. We call this ‘choking’ [1], that is they should have won but choked under the ‘pressure’
So, which team ‘chokes’ the most, and who do they ‘choke’ against??
“Guest” (or maybe i’ll have to attribute as ‘regular’..) data visualisationist [2] Matt Dick explores this in the following post
[1] Choking, despite what Wikipedia says is not the “mechanical obstruction of the flow of air from the environment into the lungs” ;0
[2] Yes I made this job title up – however apt
Without ado:
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Upsets
Matthew Dick
3/8/2016
In light of the round 2 predictions following the bookie predictions, we thought this would be a great time to start looking at bookie upsets.
What I would like to do is analyse the dataset and determine if there are any common factors where the game resulted in an upset with respect to the bookies odds.
To do this, we have merged the dataset with the player information, as well as the bookies odds, model odds, predictions etc, as well as the ongoing performance of the model/bookmaker.
There is a field in the dataset, “B_FP” which indicates bookie false positives; ie. where the bookie favoured this team to win, but they did not. To further define an “upset”, I’ve picked a bookie odds cutoff of 1:1.4 (0.71). The cutoff of 1.4 is supposed to represent a strong favourite losing the game.
The total fraction of “upsets” at the 1:1.49 bookmaker odds cutoff is around 13.6% of all games.
We can have a look at the number of times each club lost when they were strong favourites.
Assuming we are happy with the volume of data being representative, lets have a look at the percentage of bookie upsets by club, sorted from highest percentage of upsets, to lowest.
Following on from this, I thought it would be interesting to see if there are certain matchups where there a particular team causes more upsets versus another. A heatmap is a convenient way to represent this, where the Y-axis represents the bookmaker favourite, and the X-axis is the opposing side. The higher the number/brighter the cell, the more often that opponent causes upsets versus the favourite side.
For example; when the warriors have been picked as strong favourites against the Dragons, the Dragons have still won all match-ups analysed (all 2 times!).
Could a reasonable staking strategy be formed around taking a punt on the Dragons in a case like this? The Dragons odds for this game (excluding the Vig) should have been at least 3:1…Or can the NRLexp model use a “choking index” as an input parameter?