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Measuring players impact on team scoring

written by – Rangers Report 

There are many different ways to measure a player’s impact on the team’s overall success & this is yet another attempt to do so.  While going through the statistical results from last season’s Scottish Championship campaign, I began wondering what it would look like if you applied a player’s per 90 scoring stats to a team’s average performance.

It’s a simple approach & unfortunately deals with some generalizations, but screw it…

All I’ve done here is take a player’s goals + assists per 90 & divided it by the average amount of goals a team scored during the season.  Basically, how many goals did a player typically have a direct role in being scored, on average, in relation to how many goals their team generally scores.

For example, Rangers averaged 2.44 goals per match.  Kenny Miller averaged 0.83 goals + assists per 90 minutes.   His impact rating was 0.34 (0.83 divided by 2.44).  In a typical match, Miller had a direct impact on 34% of Rangers scoring either with a goal or an assist.

This is a generalized statistic in the sense that I don’t have the actual team average in games played by each individual player, rather their overall average for the season.  For example, Harry Forrester only played in eleven league matches for Rangers last season & started six of them.  In that time his scoring rate was on par with Miller’s as he averaged 0.82 goals + assists per 90 minutes & his impact rating the same as Miller’s despite playing in 21 fewer games.

Rather then be an end all, be all statistic – I see this impact rating as an entry point for performance analysts.  If a player like Forrester has a strong rating in a small sample size…I want to know more.

Did the team perform differently in the matches in which Forrester featured?  Kind of, but not really.   They averaged 2.27 goals in his matches – which is slightly worse then the average for the season.  If you wanted to dig deeper, it would be interesting to see if other players’ impact rating increased, or decreased, as Forrester came to the fore in these games.  But really, the team’s offensive production was basically on par with the rest of the season’s output.

But there are others….

Here are the results for Rangers from last season:

Impact on team scoring per 90

Given that we are dealing with per 90 minutes ratios & not the overall results, it can really highlight the impact players like Forrester had on the team – despite only playing in 31% of the team’s games.

There are two big surprises here.  Looking at the season as a whole, Barrie McKay & Jason Holt were two of the most important players for Rangers & there is no denying that fact.  But, in smaller sample sizes – Billy King & Nathan Oduwa’s impact was right there with McKay & Holt.

In King’s twelve matches, Rangers goals per match actually dipped to 1.5 – significantly less then their season average.  King’s impact rate when using the season average was 24% & when you only isolate the matches he played in it’s 39%.  Even though one goal & three assists in 12 appearances doesn’t sound like much, King was one of them most productive players for Rangers when he was in the game.

If I were presenting this data as the season was occurring, these results should spur performance analysts to hone in on these games to see what was happening.  Were certain players not matching their usual output?  What were the opponents doing tactically that impacted the team’s output & what traits did King bring to the match that had a positive impact?  Were King’s numbers being skewed by being a substitute in half of his appearances?  If so, what was different about the time King was in the match vs. the player he replaced?

On the other end of the spectrum, Rangers averaged 2.83 goals in the 15 matches that featured Nathan Oduwa.  Oduwa’s impact rate when compared to the season total was 21%.  If you applied his results to only the games he played, Oduwa’s impact rate dips to 18%.  The team, as a whole, performed better when Oduwa was in the lineup.  Was it Oduwa, or were there other factors influencing the results?  Again, this is an example of if these stats were coming to light during the season, it can help flag these games to be examined with more depth by the team’s analysts.

Nathan Oduwa

Interestingly, if you factor in secondary assists (the pass to the player who got the primary assist) the extent of Oduwa’s impact is even more noticeable.  Below you’ll see each players impact on Rangers goals when applying goals, primary assists & secondary assists to a per 90 minutes average in relation to the team’s overall goal average of 2.44.

Impact on team scoring per 90 (includes secondary assists)

Again, you see the impact Kenny Miller had on Rangers success last season.  When you add secondary assists, Miller averages 1.20 goals & assists per 90 minutes.  Now apply that to Rangers goal average & you see that Miller tended to have a direct impact on 49% of Rangers goals per match.

You also may have noticed that by adding secondary assists you see the impact that McKay’s playmaking had on Ranger success.  His impact rating before was 20% & when you factor in secondary assists it boosts up to 35%.  That’s due to the fact that McKay had a team high ten secondary assists last season.

Obviously there is only so much value to these numbers now as Rangers prepare for the new season.  But hopefully what you can see is the insight a statistic like this could provide during the season, as management (& the supporters) assess the impact each player has on the team’s overall success.

You can follow Rangers Report on Twitter @TheGersReport

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