The Curious Case of Corners

In May of 2010 I attended a Seattle Sounders match where they were beat 4-0. In an attempt to find a silver lining to such a horrible loss, the one of the local commentators pointed out that the team had earned a franchise record 12 corners on that day. The obvious implication was that while the defense was porous that day and the offense unable to score any goals, the team was at least making progress by attacking the goal and getting corners.

Fast forward nearly a year, and we're now at the end of yet another EPL season. While Chelsea stumbled through the first two-thirds of the season, they have certainly come on strong over the last third and are now in the improbable position of challenging Manchester United for the championship. As part of this resurgence, Opta noted the following statistic:

220 - Chelsea have won the most corners (220) in the Premier League this season and conceded the fewest (110). Opposite.

What if all of this commentary on corners were wrong? What if the more corners a team got compared to their opponents, the less likely the team was to win a match? This is certainly the case in the EPL, and in fact the effect is even bigger than that identified in my previous post on the effects of shots. This post will quantify how much increasing corner differential lowers the average EPL team's odds of winning a match, and compare the effects of such corner differentials for the Big Six clubs.

The Data and League Model

As in previous posts, I am utilizing data from the 2005/06 through 2009/10 EPL seasons compiled by DogFace. The raw match data has been transformed into differentials for shots, shots-on-goal, corners, fouls, and fantasy points for yellow and red cards. Match venue (home/away) is also noted. Each of these variables is then placed into a binary logistic model (BLR) to assess their effect on the odds of winning a match. BLR's were created for the league, as well as each of the individual teams that played in the league during the years analyzed. In the case of the league, BLR terms with a p-value of 0.05 or less were kept in the model. When it came to individual teams, reduced sample sizes required the use of BLR terms with a p-value of 0.10 or less to ensure a significant number of terms were retained in each team's model.

Comparisons at the league and team level were made once the BLR's were created. The graph below represents the league average home and away performance for the range of recorded corner differentials in the data set and utilizing the average values for shot, shots-on-goal, and fantasy points by venue. The dashed lines represent the lower and upper bounds of the 95th percentile prediction interval for the BLR lines. Click on the image to enlarge it.

The slopes of the lines are remarkably similar. In the -5 to +5 corner differential range, where most of the data lies, a home or away team playing to their average form experiences about a 2% reduction in their odds of winning the match for every additional corner they earn versus the competition. Notice that the two lines are separated by about 0.3 at -5 and 0.25 by the time they reach +5. This means that given each additional corner earned by a home team playing to their average form closes the gap to the away team playing their average average form by about 0.04 in this region, or 13% to 16% of the gap.

Magnitude of Corners On Match Odds

In the last post I compared the effects of shots-on-goal to shots to demonstrate that while shot differential leads to lower odds of winning, it's effects on lower those odds are only 1/3 as strong as the power of shots-on-goal to raise a team's odds of winning. The table below plots a similar relationship for corners to shots-on-goal. Teams with significant BLR terms for corners were included in the table, and it has been sorted from lowest (best) to highest (worst) ratio of coefficients. All terms are negative given the positive coefficient for shots-on-goal and the negative coefficient for corners.

Take note of the number of teams in the table - 16. This represents a 60% increase from the number of teams included in the similar table for shots differential. Corner differential has the second highest team count of any predictor - only shots-on-goal has a greater number of teams. The league average effect is that shots-on-goal have nearly three times the effect on a team's odds of winning a match than do corners. To put it another way, for every three corners that a team has compared to the opposition they must record one additional shot on goal to not negatively impact their odds of winning a match.

How does this compare to the effect of shot differential? The table below summarizes just such a comparison. Readers of my last post on shot differentials will recognize the table from that post, which has had a column added it to capture the teams' corner differential coefficients.

What becomes immediately clear in studying the table is that on a league-wide basis corner differential is far more damaging to a team's odds than shot differential is - more than 2.5 times as damaging. Also note a few clubs take a bigger hit than others. Chelsea and Manchester United realize a lower benefit from shots-on-goal vs. corners than any of the teams in the table, and see the biggest falloff compared to their shots-on-goal to shots coefficient ratio. The other teams in the table stay relatively even or go up.



Impact on the Big Six's Odds

The graphs below are just like the first one in this blog post. They utilize each team's average home and away form in the other BLR variables to allow a sweep of each team's corner differential by venue. Click on either graph to enlarge it.

A few general conclusions can be drawn from the graphs:

• Arsenal and Liverpool pretty much follow each other's form on average, both home and away. They also represent the middle of the pack in both venues.
• In both cases Manchester City has the lowest overall odds of winning a match. This is likely due to lower average performance in the other BLR attributes that would drag down their overall odds regardless of the corner differential. Recall that in the first table in this post they also had the highest SOG-to-corner differential coefficient ratio of any team. This is due to their low BLR coefficient for corners, which provides their line with the shallowest slope of any team, home or away.
• Manchester United's convex curves both home and away represent relatively consistent odds at negative corner differentials with quickly diminishing ones as corner differential turns positive. Of all the teams when playing to their average form, Manchester United is the most sensitive to corner differential in their favor.
• Chelsea's odds of winning a match when they have positive corner differentials playing to their average form away from home is simply amazing. Their odds of winning a match are nearly double those of any other team as corner differentials approach 10 to 15 corners, and they still have the better than 1-in-3 random chance of winning a match. Their odds of winning still go down, but they are the most robust to the effects of corner differential. Perhaps the Opta tweet would have been far more insightful if they had mentioned that instead of the meaningless statistic regrading overall corner differential.

Overall Conclusions

The data above makes sense if we think about what corners represent within a match. It can be assumed that corners are generated one of two ways:

1. The goal keeper or a defender makes a play on a shot-on-goal that knocks the ball out of play.
2. Under intense pressure from the opposition, the goal keeper or defender intentionally knocks the ball out of play to stop the attack before a shot can be taken.

Either way, the defense has either stopped a shot-on-goal or play that had a high likelihood of generating a shot on goal. If we know that shots-on-goal are likely to generate goals, and goals generate wins, than denying the shot attempt in the first place is likely to lower the opposing team's chances of scoring a goal as well as their likelihood of winning a match.

The higher penalty for corner differential vs. shot differential may also make sense if we think of match play in this manner. Shots often represent attempts by a player to score a goal where the shot had little chance of resulting in such a goal as it was not on target (i.e. not a shot-on-goal). Thus, the opportunity lost from simply taking an errant shot may not be that large. But if most teams avoid knocking balls out of play and generating corners for the opposition because of the dangerous nature of set plays from the corner, it stands to reason that they will not look to put a ball out of play for a corner unless it is a defense of last resort or an unintentional consequence of their actions. Thus, a corner for a team represents a bigger deprivation of a scoring opportunity than a random shot does.

What this all says is that passing, pressing, and shots have some merit but they don't matter much in a match unless they translate into shots-on-goal. And if such pressure is met with a resistance that constantly puts the ball out of play when the club on defense is at most risk, the attacking is all for nought. If the attackers keep plugging away, ensuring that their shots are actually shots on goal and that any corners are coming from shots-on-goal and not random bad passes or ricochets from the defense they will break through. That is because for every shot-on-goal that results in a corner, the team's odds of winning the match go up in a "two-and-a-half steps forward, one step back" method according to the statistics.