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A team with form of WWW may just have played 2 cup games against lower league opposition and the bottom-placed team. You need to dig deeper. The team in question for all its wins had not beaten a single team from the top 10 in the division and now was playing the one in ninth. I remember that team ending up in mid-table at the end of the season, just where the pre-season expectations had it projected to finish. Never mind what happens for a brief period of weeks as in a month long season, it is consistency that matters the most, and laying teams that just never look likely to be consistent is a great way to find that value bet.
Deeper Statistics, Shots, Possession Since most punters do not really have the time for much in-depth analysis, certain very useful stats remain unexploited and maybe that is one of the reasons why certain bets end up losing despite looking rock solid.
League tables and form are just the tip of the iceberg with form versus opposition strength going slightly under the surface, but the actual game's stats could create a perfect picture of the dynamics of performances of particular teams and players over longer periods of time.
Possession and number of shots are the first two pieces of information I look at when making in-play bets, but it is not only then when they can be useful. If teams see more of the ball, they are getting more chances to be creative, which is why I always look at possession together with number of shots and shots on target. Possession Stats I marry possession stats with shots on target to assess previous matches.
If a team dominates the ball and puts up more shots game after game, winning those games, then it is obvious this team will be a favourite and rightly so. They were on the back foot against the likes of Crystal Palace, Southampton and even relegated Newcastle but still won and that example must serve us well to see how such defensive efficiency can be observed solely by paying attention to possession and shots on target.
Of course, my own observations could be interpreted differently and it is up to the individual punter to draw conclusions on teams, streaks of results and importance of possession, but what should be a clear consensus is that these kinds of stats can only help us determine whether to trigger a bet or look elsewhere. League Statistics, Goals, Draws, History Back to the tip of the iceberg, we also have the typical league stats, some of which do play a huge part in finding value on the more obscure markets.
I always look at such simple things as the average number of goals in games, the average number of draws or historical data about leagues as the starting point when selecting games from countries I have less knowledge about. Historical data could be misleading as teams, players and season profiles change, but you would agree that a league can't turn from highly competitive to completely one-sided with favourites in a matter of a year or so.
What I mean by highly competitive leagues is such where teams are balanced and offer more draws or even better, more surprising outsider wins. While obviously interesting, they are also more difficult to figure out and require that additional time in discovering the dynamics.
As for goals, you can't expect a league where the majority of teams have averaged less than 2 goals a game in the past seasons or who rarely hit more than 5 shots on target a game to suddenly turn into a high-scoring league. It is worth monitoring those as they are incredibly consistent over long periods of time and if you have not done much betting on them in the past, you might be tempted by the value of certain markets without understanding why is it so.
French League 2 is what immediately comes to mind as I have been doing a lot of work and even published blogs on it in the past. This league has been dictating unders on the goal market for ages and bookies value those unders accordingly. Historical Data Looking back at say the last 5 seasons of a particular league can reap rewards when it comes to statistics. Lastly, while historical data on leagues is useful and interesting to analyse, historical data on teams I consider a double-edged sword.
I had already touched on this topic in a prior post see On the Probability of Scoring a Goal. In this post, however, I have expanded my dataset, and in addition will do my best to illustrate my point with the raw data. The results from the previous post were the end result of a regression analysis, and somewhat of a black box from the point of view of the reader. I will try to be more transparent here. The play by play data from these matches is merged together with pre-game betting odds, courtesy of OddsPortal.
A Spurious Correlation In a paper , a team of German physicists argued for the existence of what they called "self-affirmation" of goal scoring in soccer. I have a knee-jerk skepticism for these " just so " types of explanations, especially ones that resort to armchair psychoanalysis of the athletes playing the game.
I find it hard to believe that professional athletes, having spent a lifetime training to play at the highest level of their sport, react like sullen teenagers in response to a temporary in-game setback. And like any good "just so" story, we can create an equally plausible explanation for the opposite result: Shouldn't teams be more motivated to score when trailing their opponent?
My hangups aside, what does the data say? The German authors used game results from some 40 years of German league play, but had limited data with respect to timing of each goal. As mentioned above, my dataset only goes back two years, but draws from a more diverse collection of leagues. I also have much more detailed data on when goals are actually scored in-match. Using that dataset, I have charted per-minute goal frequency as a function of game time. There are two lines: one for teams that are trailing by a goal, and another when leading by a goal.
Goal frequencies are averaged in five minute buckets to keep the chart from getting too noisy. At first glance, this graph seems to support the "self-affirmation" theory of the paper. Teams leading by one goal appear to have a slightly higher probability of scoring another goal, particularly after minute But this is just correlation, not necessarily causation. Does being up a goal really motivate a team to score more? And de-motivate their opponent?
Or is there an underlying variable at play here? Ice cream leads to murder and running the football leads to victory A commonly used example of the correlation-causation fallacy concerns ice cream sales and the murder rate. When ice cream sales rise, there is a corresponding increase in the murder rate. But ice cream doesn't promote homicidal tendencies, and committing a murder doesn't induce cravings for a triple scoop waffle cone as far as I know.
The actual cause is warmer weather, which leads to both ice cream sales and higher crime including murder. See below for an illustration.
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The fluid nature of the game meant that for most of its existence nobody officially recorded anything other than goals. Coverage of soccer is now stuffed with the kinds of statistics that might mean nothing at all, a proliferation of RBI but precious few WARs, so to speak.
Most of the best statistical work is going on quietly, behind the scenes, where sharp minds both inside and outside the game sort through all the data and build the important tools that actually hold predictive power.
Normalize stats, even basic ones such as goals and assists, for the amount of minutes a player is actually playing. This is particularly important on the attacking end of the field. Their raw numbers, or their per-appearance numbers will make them look like mediocre attacking players when in fact they are being incredibly productive.
Shots It sounds obvious, but the number of shots a team takes and concedes turns out to be really important, and a fairly predictive measure of future success. Building on the work of hockey, early soccer analysts used tools such as Total Shot Ratio and PDO as tools for predicting future success based on past shots, and it works quite well.
Using shots means more data, and that data is surprisingly predictive. There are, of course, good shots and bad shots. And some teams are better at taking more of the former, and others rely heavily on the latter.
Shots are similarly useful when evaluating attacking players. Almost all great attacking players take a lot of shots. The idea of the player who is relatively conservative about firing away but is a deadeye when he hits the ball is largely a myth. Part of what makes great soccer scorers great is that they are able to get their shots off.
Similarly to evaluating teams, it can be tricky to account for players who take a lot of bad shots. But, after accounting for that one useful way is to also look at shots within the yard box as well as total shots , looking for players generating a lot of shots is a good way to find guys who are going to score a lot of goals. The Common Mistakes Shooting Percentage: Players who score on a high percentage of the shots they take are not necessarily good shooters. Juventus forward Cristiano Ronaldo poses at the Allianz.
They are. But, over the course of a game or a season, or five seasons, the amount that those skill differences matter pales in comparison to both the amount of the kinds of shots they take matters, and blind dumb variance. Good teams tend to have high possession statistics. That is because good teams tend to have good players, and good players tend to be good at doing things such as passing the ball accurately to other good players, or taking the ball from bad players on the other team.
But, playing a high-possession style does not, in and of itself, make a team good. There are plenty of club teams, such as Chelsea or Atletico Madrid that have chosen to play a style that focuses on defending without the ball and counterattacking effectively. Those teams maximize their personnel and tactical choices to effectively execute that style, and they are perfectly capable of staying at the top of European soccer by doing that.
This is particularly important in international tournaments where teams are formed by nationality and not by professional planning. Collections of talent are unavoidably awkward fits, as opposed to being cohesive units. The top teams in the world such as Germany, Spain or Brazil all fit together and are happy to be possession machines. But for many others, the talent pool leads to adopting defensive strategies that downplay possession in favor of conservative positioning.
Trying to judge middle-tier World Cup squads by their possession percentage is a recipe for disaster. The Advanced Stats Expected Goals Expected goals remains the gold standard for predicting future performance. Conceptually the idea is simple.
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