While bathed in controversy, the GWS-Collingwood 2019 preliminary final was a clear case of “next goal wins.” The Pies had the last nine scoring shots, kicking 4.5 to 0.0 after the 5:30 mark of the 4th quarter. From 27:57 to the sound of the siren at 34:57, GWS held a 4-point lead.<\/p>\n\n\n\n
Even with Collingwood’s momentum, predictive models showed GWS were always predicted to win the match in the 4th quarter, even at a one-goal margin without any momentum (though momentum is overrated statistically.) This was a curious problem for me, as the expectation would be the match was 50%-50% late on, especially after two late behinds which would have given Collingwood a lead with another goal, but the computers always gave GWS a good 60% chance of winning at worst. Doing some fairly simple simulation, I wanted to determine whether this would be accurate – what percentage chance does a team four points down have of winning the game at a specific time?<\/p>\n\n\n\n
Four points is an interesting question, since it more or less removes the chance a behind would be scored that would influence the outcome of the game. Let’s look at some assumptions:
– 4739 goals were kicked in the first 206 games of the 2019 AFL season, or about 23 a game, nearly on the number.
– 3513 behinds were kicked, about 17 a game.
– At 23 goals\/game, the odds a goal will be kicked in a given second are 23\/4800 (number of seconds in a match), or 0.0047%, or about 1 goal every 3.54 minutes. Of course, the odds of whether a goal will be scored in a given second vary wildly based on the pitch location of the ball, but this is a nice average for simulation purposes. (If a goal is scored in one second, the next second will have a 0% chance of producing a goal, but this was not built in.)
– Only one goal can be scored in a given second – an easy assumption, more of a rule, perhaps.<\/p>\n\n\n\n
Assumptions in hand, let’s now take a single pinpoint of a match: the 17:00 mark of the 4th quarter, ignoring time on (TV time counting down from 3:00.) The away team has a four point lead, and the teams are exactly equal in strength. Running our simulation 100,000 times shows the away team will win the game 74.4% of the time, with the home team kicking the goal(s) they need to win 25.6% of the time. In 40.7% of games, no goal is kicked by either team. If my math is right (away team win probability when a goal is kicked = 74.4% – (100% – 40.7%)), that means in games where at least one goal is kicked, the team with the lead still wins 56.8% of the time.<\/p>\n\n\n\n
But what if the teams aren’t equal? The line on the Collingwood-GWS game was Collingwood -21.5, or about three and a half goals. That means Collingwood’s rough expected goals using the average goals\/game calculated above would be approximately 13.25 to 9.75, or a 79.5 to 58.5 win (ignoring behinds). This would weight Collingwood as having a 57% chance to score any given goal in a game.<\/p>\n\n\n\n
Running the simulation to give Collingwood a 57% chance of kicking a goal only decreases GWS’ odds of winning to about 69.9% from 74.4%. In this simulation, “only” 40.6% of games didn’t feature a goal, functionally the same as before. This number’s important, however – if this event happens, GWS win! Even if Collingwood are weighted to kick goals 100% to 0% of the time, GWS still win the game no less than 40.6% of the time.<\/p>\n\n\n\n
Given the assumptions baked into the simulation, Collingwood only win 50.7% of the games in which a goal is kicked. A true 50-50 game – but only if at least one goal is kicked!<\/p>\n\n\n\n
Obviously there’s a lot of potential for error in this simulation, including not simulating behinds. The rate at which goals are scored may increase in high leverage situations, for one – I haven’t looked into leverage but would like to. Still, if your team is up by 4 with 3 minutes to play, and it feels like a “next goal wins,” it’s quite likely a “next goal wins”, but because of the high likelihood no goal is scored, it’s not a 50-50 game.<\/p>\n\n\n\n
The odds of winning also go up steadily as the number of seconds in a simulation gets closer to zero. Given a starting 52-56 scoreline, at two minutes, GWS’ win rate gets up to 76.5%, at one minute it’s 86%, 30 seconds 92%, and 15 seconds remaining interestingly only 96.1% (as there’s a 6% chance of a goal being scored in any given 15-second span.)<\/p>\n\n\n\n
Of course, with the benefit of hindsight, if Collingwood had kicked a goal, the 2019 grand final would have been 99% more likely to be interesting to a neutral. That being said, it does support the fact that what feels like a 50-50 game will be estimated correctly by a predictive model if it favours the winning team. <\/p>\n","protected":false},"excerpt":{"rendered":"
While bathed in controversy, the GWS-Collingwood 2019 preliminary final was a clear case of “next goal wins.” The Pies had the last nine scoring shots, kicking 4.5 to 0.0 after the 5:30 mark of the 4th quarter. From 27:57 to the sound of the siren at 34:57, GWS held a 4-point lead. Even with Collingwood’s … <\/p>\n