I just had a quick look at the times goals were scored in the Premier League dataset:

If you'll excuse the somewhat poorly labelled x-axis (R is being fussy, and I'm not particularly inclined to try and fix it for something so trivial), there are a few interesting points.

Most obvious are the two huge bars at around half time and full time. Of course, as you might have already guessed, this is due to how goal times have been reported - goals in injury time in the first half are reported as a 45th minute goal, and in the second half as a 90th minute goal. The fact the 9oth minute bar is so much taller than the 45th minute one demonstrates something most of us have probably observed: second half injury time is almost always longer than first half injury time.

If we filter out these two anomolies, and look at the goal times without the 46th of 91st minute goals, we get the following:

A couple of things to notice here. The first is that goals in the first minute really do seem quite rare, occurring in just 82 games (once in over 200 games). The second is that there looks like there might be a slight pattern to goal times - it seems goals become a bit more likely as the match wears on. Is this the case, or are our eyes just deceiving us?

Fortunately, we can test this hypothesis using a statistical technique called linear modelling. What this means is that we assume that the number of goals scored for a particular minute have a linear relationship with the time at which they're scored. In other words, if we just plotted the above bar graph but with points instead of bars, the points would lie on a roughly straight line. In fact, let's do this and see how it looks. One thing we'll change is from goal frequency to percentage, the data are the same, but it will make more sense to talk about percentages later on.

It looks a lot clearer when we plot the data this way, with most of the points seeming to lie (very roughly) on a straight line. We also note that each minute seems responsible for around 1% of goals. With 90 minutes in a game we'd expect something like this, so we've provided ourselves with a useful 'sanity check' - never a bad thing when playing with data.

Having observed this pattern, can we make any use of it? It might be nice to be able to fit a model that could tell us how likely a goal in, say, the 10th minute of a match would be, if it's true that there is a simple, straight line relationship between time in the game and likelihood of a goal. One option would be to just put a ruler on the graph and try and draw a straight line that seemed to best fit the data (indeed, I can remember doing this when I was at school, back when I would call this a 'line of best fit'). Fortunately, we can use statistical software to do this for us, and in arguably a much more reliable way.

To put our model mathematically, suppose the percentage of goals scored in the Xth minute of the game is G, then we'd assume that G = a + bX, where a and b are some numbers we want to find out. Using statistical software we can fit this model through a method called least squares). What this method does is a bit like what you do when you put a ruler on the graph and try and move it around until it looks about right, with the same number of points above and below the line you want to draw. What your eye is doing when you do this is probably trying to minimise the total distance all of your points are from the line you're drawing; what least squares does is calculate the line that minimises the square of these distances. In other words, if you imagine a line drawn on the graph, and measure the distance from the line to each of the points, square these distances and add them all up, least squares will find you the line that makes this total the smallest.

Applying this method we find that a = 0.895 and b = 0.005, which we can then plug back into our model equation: instead of G = a + bX we now get G = 0.895 + 0.005X. This tells us that for every extra minute in the game, the percentage of goals scored in that minute goes up by 0.005. Admittedly, this isn't very much at all, but over the course of the game that works out to around a 0.45% increase from start to finish, which when you consider that the average minute will only have 1.11% of goals, seems a little more dramatic. For example, a goal in the 80th minute is 40% more likely than a goal in the 10th.

Finally, let's plot the line onto the previous graph, like so:

You might well think that it looks about as good as something you could have done by eye with a ruler, and you're probably right. However, another thing using a computational method can tell us is how 'significant' the effect of time on goal probability is. In other words, to what extent is there really an underlying relationship between the time in a game and the probability of a goal, and to what extent is this just the result of our data randomly falling into a pattern. We find that, mostly thanks to the sheer size of our dataset, that this pattern is not likely to be down to chance; there really does seem to be a linear relationship between time in a game and the probability of a goal going in.

Perhaps something to bear in mind after a goalless first half.

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Great post just wondered if you ever worked out the probability of what time the first goal is scored in a premier league match.

ReplyDeleteie

1-10 11-20 21-30 31-40 41-50 51-60 61-70 71-80 81-90

It's fairly simple to calculate those probabilities. Assuming a continuous probability function G = 0.895 + 0.005X (in percent), and assuming that the 10 minute periods are independent with regard to the probability, simple integration will give approximately these figures for the time of the first goal:

Delete1-10: 9.2%

11-20: 8.8%

21-30: 8.3%

31-40: 7.6%

41-50: 6.7%

51-60: 5.7%

61-70: 4.6%

71-80: 3.2%

81-90: 1.6%

The numbers have been rounded. Clearly, they don't add up to 100 as teams often go goal-less. Also, they use the linear approximation derived in the article and therefore do not account for injury time.

Not so often. These assumptions must be wrong as they implicate that overall chance for non 0-0 results are 55,7% (sum) plus injury time first-goal chances, while its known that probability for no goals in match are 15% and less. So it looks like 100-55,7-15 = 29,7% chance for first goal in 1st or 2nd half injury time? Impossible.

DeleteWhere did you get this data?

ReplyDeleteIm just trying to figure out when the winning goal will most likely be shot

ReplyDeleteI wonder if the goal probability increases gradually over time or if it's the scoring of the first goal that causes the game to be more open (and so more goals to be scored)

ReplyDeleteThis could be settled by looking at the timing of the single goal in 1-0 and 0-1 games

The first minute has fewer goals because at the start of it, it's a kick-off and none of the players are in goal-scoring positions. Ditto for the 46th minute -- you can see a dip in minute 46 in the top bar graph. However the effect, for reasons I can't fathom, is less pronounced in minute 46 than minute 1.

ReplyDeleteOne consequence: Goals are 1.5 times more likely in the last minute of a game than the first minute.

ReplyDelete(Formula: (0.895 + 0.005*89) / 0.895)

I calculated that goals are scored after the 88th minute in about 10-15% of all matches. I tested this by placing bets in play on no goal scored after 88th min, I won 14 bets in a row at 1/5 - 1/10 odds before I lost, I staked £10 initially and built up to £125, I lost one bet at 1/5 and then incredibly unlucky I lost another at 1/7. Thus losing 2 bets in a row.

ReplyDeleteOne was in prem, 7 matches played, 2 matches had goals scored after 88th min, giving me about a 30% of losing!!!!

Hey, I'd be interested to know if you kept on doing this and how you're fairing so far?

DeleteOk so I don't understand something, as the article there is 15% chance for a goal in first 15 mins, but as the statistics 30% chance for a goal in the first 15 mins.

ReplyDelete