There seems to be a thought going around Columbus Crew SC circles that Ola Kamara is a poor finisher.

Despite the stats of 30 goals in 53 games, some fans seem to think Kamara should be doing more.

The question is, aside from watching a play and thinking “oh, Kamara missed a very easy goal that he shouldn’t have,” how do you quantify whether the forward misses more easy chances when compared to his MLS peers?

The goal with this piece is to determine whether this thinking is correct or whether the narrative of Kamara’s poor finishing is an example of confirmation bias and therefore, a myth.

Reviewing each of Kamara’s (and many other strikers) shots from this season would be a time-consuming task. So, we will use a two different metrics to determine whether Kamara misses easy chances and compare him with his MLS peers.

The metrics that we will use are:

Expected goals (xG)

Scoring Chance Percentage (SC%)

xG

Expected goals, or xG, is an advanced metric that assigns a decimal value (0.10 = 10%) to every shot that a player takes on the field. As per American Soccer Analysis’ really good explanation, “(xG) are the number of goals that can be expected to be scored based on where and how a shot was taken.”

So, a shot taken from one yard out will be assigned a higher decimal value/percentage than a shot taken from 20 yards out.

xG is valuable in that it’s purpose is to show how many goals a player should score from the places where the player takes the shots. This metric does have its shortcomings (for example, some teams play a style that results in fewer or more goals being scored than the team xG), but it remains the most widely used “new” statistical model to evaluate offense.

Here is how some view Kamra: the Crew SC striker scores incredible goals (a few of these stand out) while missing very easy finishes. If this is true, the difference between Kamara’s goals (G) and xG total should be a negative number.

“But he scores goals that have a 0.00001 xG (very low probability of scoring),” you might say. Sure, but not all 14 of his goals this season have such a low xG. Additionally, if he’s missing so many sitters, then he should still have a higher xG than G.

Through 28 matches, Kamara has 14 goals and his xG is 13.93. That means, the difference between Ola’s G and xG is 0.07. More simply, the Columbus striker has scored almost exactly as many goals as he was expected to.

Let’s compare this to a few other MLS strikers. New York City FC’s David Villa has 19 goals and an xG of 12.33. That’s a difference (G-xG) of 6.67, suggesting Villa is sometimes lucky, scoring really difficult goals or some combination of the two (that 6.67 difference is the second-highest among the top 25 goal scorers in MLS, behind Ignacio Piatti’s G-xG of 7.85).

Looking at a striker on the other end of the spectrum, Portland Timbers’ forward Fanendo Adi has 10 goals on this season and an xG of 11.59. That’s good for a top-20-goal-scorer-highest -1.59 G-xG. This suggests that Adi is either very unlucky or might be the striker who is actually bad at finishing off his chances.

So, from the perspective of G-xG, Ola Kamara is an average finisher who finishes the chances that the metric thinks he should.

SC%

The next metric we’ll use is scoring chance percentage (SC%). SC% is simply the number of goals scored by a player divided by the number of shots (G/S). This is a relatively self-explanatory statistic that shows what percentage of a player’s shots find the back of the net. A player with a high SC% scores a higher number of his shots than a player with a low SC%.

The biggest weakness with SC% is that it tends to disfavor players who are the focal points of their team’s offense. If a player has to carry the offensive burden alone, he will obviously take more shots and have a lower SC%.

If Kamara does miss multiple easy chances, then his SC% should be near the bottom of the top 25 scorers in SC%, and near the bottom of strikers with between 60 and 70 shots.

As of publication, Kamara is tied for sixth in goals (with 14) and sixth in shots (with 72), and has an SC% of 19.4% which is good for 15th). This SC% of 19.4% is just 1.4% behind Minnesota United’s Christian Ramirez in 11th with an SC% of 20.8%. Piatti leads Major League Soccer with an astounding 28.3 SC%.

Here is a list of the six players who have between 62 and 72 shots and their SC%:

1. Ola Kamara (72 shots; 19.4 SC%)

2. Diego Valeri (71 shots; 22.5 SC%)

3. Bradley Wright-Phillips (70 shots; 20 SC%)

4. Freddy Montero (69 shots; 15.9 SC%)

5. Fanendo Adi (66; shots 15.2 SC%)

6. Miguel Almiron (64 shots; 12.5 SC%)

Arranging these attackers in order of SC% results in: 1. Diego Valeri; 2. Bradley Wright-Phillips; 3. Ola Kamara; 4. Freddy Montero; 5. Fanendo Adi; 6. Miguel Almiron. Kamara falls right in the middle of this pack of peers. In 2016, Kamara finished the season with an SC% of 20%, which is nearly identical to his current number.

Maintaining the comparison, Villa, MLS’s leading scorer, currently has an SC% of 17% (just slightly lower than Kamara). When compared with Villa’s G-xG of 6.67, it’s possible to hypothesize that Villa is a high-volume striker who might be scoring on some incredibly difficult shots (this conclusion comes from comparing a lower SC% with a higher G-xG; the higher G-xG suggests that his goals are from relatively low percentage shots, and the lower SC% suggests that he takes relatively low percentage shots). In 2016, Villa had a G-xG of 2.68 and a much lower SC% of 13.9%, which supports the hypothesis that Villa scores on difficult shots.

Continuing the comparison with Adi, the Timbers’ striker has an SC% of 15.2 % which, compared to his -1.59 xG suggests that Adi might have a problem missing easy shots. Adi finished 2016 with a -2.52 xG and an SC% of 18.2%.

The most surprising SC% is of MLS darling Sebastian Giovinco. The Toronto attacker has an SC% of 13.2%, which is good for 23rd among the league’s top 25 scorers. In 2016, Giovinco finished the season with an SC% of 9.6% but did lead the league with 177 shots (Villa was second in shots with 166).

From the perspective of SC%, Kamara scores on an average percentage of his shots as strikers with similar numbers of total shots, suggesting again that he’s an average finisher.

Conclusion:

If you compare Kamara to other top 25 scorers, it’s clear that he scores about as many goals as he is expected to and scores on about as many of his shots as other strikers with a similar number of shots. It’s easy to watch a match and thing “Kamara should have hit that easy shot,” but the statistics do not back it up.

There just isn’t any empirical data to suggest that Kamara misses simple goals while scoring his more difficult chances. Simply put, Ola Kamara is an average finisher among the top 25 goal scorers in MLS.

Myth.