Investigating Total Goals Lines (Part I)

While the Asian Handicap market is known as the smart place for traders to operate, there is a significant sway towards Asian Handicap lines rather than Total Goals lines. Natural bias may be one reason for this, as traders will often have a preference towards one team over another, and if the line is lower than what they might expect they use that apparent edge to execute a trade.

In general the Total Goals lines are often seen as harder to call and more open to interventions of “luck”. The lines themselves are usually set between 2.25 and 2.75 in the major European leagues, and while some teams are seen as being prone to being involved in “overs” games or “unders” games, the slightest thing can throw pre-match expectations into disarray.

To give an example, the Queens Park Rangers versus Norwich City game from the 19th of November positioned QPR as +0.25 underdogs on the Asian Handicap pre-match, mainly due to Ian Holloway’s return as manager and Norwich’s poor form, while the Total Goals line was 2.5. At the time this actually seemed slightly higher than expected, because Rangers were also out of form and were playing a “better” team, so may have tried to keep it tight. In addition to this Norwich were notable for scoring many of their goals from Fairly Good and Poor Chances, something that can only continue for so long.

The game began and within 45 seconds Martin Olsson of Norwich had handled the ball on the line, given away a penalty and been sent off. Had the trader known this was going to happen before the game, they would certainly have changed their view of whether to go over or under the Total Goals line.

While unforeseen circumstances such as these are part of the nature of sports trading and will rarely be avoidable, I wanted to use StrataData to look at how Total Goals lines were set, whether there was any pattern to them being Over or Under and how the chance data we collect could be used to determine whether an advantage could be gained. My belief is that even the smallest advantage could provide the edge most traders are looking for.

To begin, I took all of this season’s data up until Thursday 24th November from the “big five” leagues, as well as the English Championship, Portuguese Primeira Liga and Dutch Eredivisie. The first thing to note is the average goals per game so far in the leagues mentioned:


This shows Spain and Germany having the most goals per game, with almost 3, while as expected the Portuguese Primeira Liga brings up the rear with only 2.35. The one mildly surprising thing to note is that the Dutch Eredivisie is fifth of the eight leagues investigated, largely because it is generally thought of as one of the more high scoring competitions in Europe.

Looking at the Total Goals lines for each competition throws up some more interesting data, but using the average Total Goals line would not be useful in this situation. Teams like Barcelona, Real Madrid and Bayern Munich all have high Total Goals lines for almost every game they play, which would skew the data quite heavily. Instead I looked at the median and mode to determine which is more useful to use. While the results were similar in most cases (only three leagues had a difference between their median and mode) I chose to focus on the most common line, as this gives a better representation of how the league is thought of by the market in terms of goals:


Despite having the highest average goals per game, the most common line in Spain has been a measly 2.25. This shows just how much Barcelona and Real Madrid can skew the average number of goals with their averages of 3.75 and 3.67 per game respectively. I noted my surprise at the Dutch Eredivisie, which was largely because it tends to have a relatively high common Total Goals line of 2.75 on a week-to-week basis. While there is again some skew due to Ajax, Feyenoord and PSV it is still generally thought of as a high scoring league. Interestingly, five of the eight leagues had their most common Total Goals line as just 2.25.

The market will often pitch these lines quite nicely, but just how often do the actual scores beat the Total Goals line? To look into this I have counted any score over the Total Goals line as a full win, so even if there were 3 goals scored in a game with a 2.75 line it just counts as a win (rather than a half). The same applies to losses Under the Total Goals line (so if there were 2 goals in a game with a 2.25 line it just counts as a loss, rather then a half):


Only two of the eight competitions are over the Total Goals line more than 50% of the time. This is a clear indication that despite higher average goals than the pre-stated Total Goals lines there is perhaps more value in picking unders rather than overs in general. Teams in French Ligue 1, Dutch Eredivisie and the Portuguese Primeira Liga are only hitting overs around 40% of the time; which is in spite of two of these competitions having their most common Total Goals line at 2.25!

This again shows just how much the top teams can skew the average number of goals in each league. The better teams will always score more goals, but when this happens it will almost always then see an increase in their individual Total Goals line the following week. The inverse can be true for poorer teams at the bottom end, but there is rarely such a significant decrease in the Total Goals lines for those teams. Using the Barcelona and Real Madrid example again, their most common Total Goals lines were 3.75 and 3.5 respectively, which you would think would be reflected in some very low Total Goals lines for a bottom of the table team like Granada, for instance. However, while their most common line is 2.25 this also matches the most common line for Spain, meaning it is not significantly below the mode.

For the second part of this piece I am going to investigate some of the factors that drive Total Goals lines and look into how the granular chance data we collect reflects against these. In case you missed it, the methodology behind how we collect chances was previously covered in great detail here.

I believe it will be interesting to see if games with higher Total Goals lines are reflected in both a higher number of overall chances and also a higher number of better quality chances. Ultimately, by using StrataData I hope to show that we can better judge whether a team is more or less likely to beat the Total Goals Line in any given game.

Dave Willoughby (@donceno)

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