# 2016 U.S. Women’s Soccer- Blame it on the Rio Olympics- Should Have This Team Been There? Part 3- What a SNAFU!

#### By Steve Fowler

## 2016 U.S. Women’s Soccer- Blame it on the Rio Olympics- Should Have This Team Been There? Part 3- What a SNAFU! A look at the USWNT and NWSL statistically.

The previous articles from this series, the “Blame it on the Rio Olympics” Parts 1 and 2, look at evaluating the U.S. Women’s National Team (USWNT) players statistically to see if the best players from the NWSL were taken to the 2016 Rio Olympics. In Part 3, players are analyzed once again with a different, but now much improved method. In this review we will be statistically evaluating players by “SNAFU”.

Now, this is NOT the old World War II phrase- “Situation Normal- All Fouled Up”. Nope, this is “Statistically Normalized- All For ‘U'”! Actually, that is such a horrible and corny name, so how about “Statistically Normalized – All ‘Fudged’ Up”? After all, I do use a few “fudge factors” to make the statistical math work. Again, this is not an “official name”, it is just a quick name for convenience.

I wanted to be able to use a statistical method that can be used to evaluate NWSL players over the course of a season. But I also wanted to be able to compare teams in a head to head matchup. That is what I hope to accomplish with “SNAFU”. In the next few days I will be reviewing the Sweden versus USA in the 2016 Olympics Quarterfinal. But here, we will use “SNAFU” to evaluate NWSL players to see which players “deserved” to go the Rio Olympics.

## Stat Geek Warning! This article contains over 5000 words and a few really bad jokes. This article might take about 15 -20 minutes to read.

In case you want to see the prior statistical analyses.

But here is a video in case you are bored already.

## Now that I have probably “jumped the shark” (^^^) in this episode, let us begin…

## Introduction

I took the statistics from roughly the first half of the 2016 NWSL season from a site called WoSo Stats and also from the NWSL league website (links are in the ‘Sources’ below). There are 10 Categories and each is worth approximately 10 points. I set the average of each category to about 5, hence my loose definition of “normalized”. Therefore, a score of 50 points means that you are average. A perfect score of 100 means that you are the greatest player ever. A score of 40.25 in this index means that you at least showed up to practice and suffered all of the games on the bench. A score in the 30’s means that you definitely should have stayed on the bench. The categories all have different ways of calculation.

## Preliminary Statistical Overview

This was a “quick and dirty” calculation, no official statistical processing was done. By rights, a good statistical process should have something like a T-test performed to see if your data is normal. And that is just the first step before you can do anything fancy like a correlation or regression analysis in order to make accurate calculations with the data. I am not a professional statistician, but I wanted a calculation that can evaluate players over a full season or compare game to game.

## “Should have there been ‘hard-core’ statistical processing done”?

The answer actually is NO. What we have for data is half of the 2016 NWSL season or about 50 games. So, to test what statistics there are, I tested a couple obvious goal scoring opportunities, “through balls” and “crosses” that were completed, not overall attempts. If you get low correlations and regression coefficients with these categories, then essentially, any other correlation for “possession skills” or “defensive skills” will be a zero correlation, and a complete waste of time.

## So, here is my quick data table:

Category 1 | Category 2 | Correlation coefficient | Regression R-squared coeff. |

Through Balls | Goals | 0.2307 | 0.0532 |

Through Balls | Assists | 0.4201 | 0.1765 |

Crosses | Goals | 0.2302 | 0.0530 |

Crosses | Assists | 0.4671 | 0.2182 |

I also did a quick correlation comparing assists and corner kicks and got a 0.48 correlation. Unfortunately, my limited statistical knowledge tells me that these are weak correlations and terrible regression R-squared coefficients. Any use of the regression slope and Y-intercept in an effort to come up with a fancy statistical calculation similar to what was done with the McHale, Scarf, and Folker would be a huge investment in wasted time. The reason why there are weak correlations above is simple, there are lots of ways to score goals! Heck, even the other team can score goals for you with an “own goal”. But notice that the correlations are much stronger for assists. Therefore, it is likely that these were actual assists that led to the higher correlation coefficients. The problem is not everything leads directly to an assist either.

Therefore, it would be far more productive to try to come up with a “logical analysis”. In other words, the statistics should have a meaning and purpose. The system albeit will not be “perfect”, but the overall purpose is for comparing player skills and overall team performance. You want to see some differences, but then again, you do not want one factor dominating to skew the results of players with only one skill set.

## “SNAFU” Introduction

The overall theory behind the numbers is that if you help with a goal then you should receive a point. Therefore, an assist is worth a point. Crosses and corner kicks that are completed are almost a point as you are giving your team a chance to score. A shot on goal is also worth a point. A goal is now worth 3 points. 2 points for the goal itself, and a point for the shot on goal. This may not be intuitive at first, but this system can be looked at to evaluate overall team performance. I will describe team comparisons with more detail in the “Conclusions”

So, a “goal point” overall would include all of the dribbles, defensive work, and attacking passes that eventually led to the goal. So, in theory, a team averaging 51 points should have a goal advantage over a team that has players averaging 50 points if they were to play using these statistics.

## “Possession fudge factor” explained.

Many of the parameters below deal with “possession”. According to my math, about 15 possessions are equal to a goal (or about 6.7% of the time). I looked at total goals in the league and did a simple comparison to the categories listed. There were about 15 “possessions or defensive stops” for every goal. For example, there were about 16 “tackles” for every goal, and about 15 times per goal for every “dribbled” . The number “15 or 16 times” for every goal did not always apply to all parameters. However, there was enough evidence to indicate that a possession change every 15 or 16 times might lead to a goal or at least a chance at goal.

So from above, using the theory if an assist is now doubled to become a point, then once out of about 7.5 possessions should have a “chance” at goal (or roughly 13.4%). The number 0.134 now becomes my “default number” for most statistics dealing with “possession”.

So, if you lose the ball to a defender you are deducted 0.134 points. Steal the ball right back and you gain that 0.134 points back. There are some exceptions to this, but overall, this is a “rule of thumb” for the “SNAFU possession calculation”.

## Categorical Fudge Factor explained.

If you notice in the Category descriptions below, most categories are sums that are adding to “4.5”. Why this number? The reasoning is simple. I wanted the overall average of the categories to be about 5 points each. Therefore, if you “do nothing” all game long, in theory, you are hurting your team. So, with a minimal amount of effort in each category, then you are at least close to average,

## See the below categories descriptions for how each category, and all of the parameters are calculated. Keep in mind that the below calculations were set to “per 90 minutes” for each parameter to get a season average for each player. This means now you can use the same exact calculations for comparing teams in a single game as the overall results would be the same.

.

## Here are the 10 categories in no particular order at all.

## 1. Appearance

## This has nothing to do with “looks”. This is the amount of time played on the pitch. Reward players who play.

- Preliminary calculation is Minutes Played divided by Team Minutes.
- Multiply above by 10.
- Therefore, if you played every minute, you receive a “10”. Or, if you sit on the bench the entire season, you get a “0”.

## Note:

When comparing teams either “head to head” in one game or in a season long comparison, this statistic is “dropped”. When comparing averages of teams, “bench players” tend to dilute the overall results with this statistic. “Appearance ” is only good for comparing players as the amount of time played should be a factor. Obviously, you do not want a player with minimal playing time to be rated “number 1” if they only played 15 minutes that season.

## 2. “Personal Team Suffering or Punishment”

## I do not have a great name for this. Yes, this is how much “pain” you “inflict” on your team. Reckless fouls, careless yellow cards, red cards, and own goals all cause pain to your team. I looked at several parameters to see ways that a player can hurt or punish their team.

## Parameters and their math is below:

- Red Cards- each is worth 1 point. You are essentially surrendering 1 goal with a red card.
- Yellow Cards- worth 1/2 point each. As you know, 2 yellow cards equals a red card. Also, a yellow card should be worth more than an ordinary foul. A player with a yellow card has to play more conservatively as a second yellow card means that they leave the game with no replacement. Also, and I have no proof of this, but a yellow card must have a psychological effect on the players around them. So, other players might be “looking over their shoulder” at the player with a yellow card to make sure that they do not get into trouble again.
- Net fouls- Fouls Suffered minus Fouls Committed. Obviously if you commit a lot of fouls then you are hurting your team. The total is multiplied by 0.125 as I estimated that it is slightly better for the team (as compared to losing possession at 0.134). Most fouls are from the defensive side of the ball, so they are not losing possession. But, a foul should be punished as this leads to a free kick, which is a set piece. See the “source notes” listed in the Set Piece Category description to see why giving the opposing team a set piece opportunity is bad.. On the other hand, if you suffer a lot of fouls, you are helping your team in field position and set pieces with free kicks.
- Net penalties- Penalty Kicks Won minus Penalty Kicks Conceded. Obviously, you do not want to be that defender that fouls the player dribbling in the penalty box. You are causing the team to lose the game on a penalty kick. But, also, you can reward your team if you were the one fouled to give your team the penalty kick. Worth 1 point (plus or minus).
- Offsides- This is multiplied by 0.134 If you were offsides, you lose possession.
- Own goals- You just gave the other team a goal! Minus 2 points there.

## Overall Calculation:

- Assuming that you are an average player who does not hurt, nor help their team, you are worth 5 points in this rating system. Add 5 points to the above parameters. Remember you are subtracting the points from red cards, yellow cards, offsides, and own goals. Net penalties and net fouls can be plus or minus.

## Note:

Therefore, if you are really bad, in theory, you can get a negative score here. A non-National team player listed on both the McHale et al. and the “KISS” lists fell about 70 places in the overall ranks due to this category alone. Obviously, she did not make the recommended player list this time. With this calculation alone, “SNAFU” already has an advantage over the prior 2 statistical systems.

## 3. Goalkeeping Rank

## Not much effort was put here, but I wanted a simple but effective way to distinguish the goalkeepers. I split 2 categories worth 5 points each.

- The first part is 5 points minus Goals Against Average.
- The second part is Save Percent times 5.
- Add up the scores up to put goalies on a scale of roughly 1 to 10. Obviously, if you are not a goalkeeper, you receive a “0”.

## Note:

The goalkeeping results of “SNAFU” are actually encouraging. Abby Smith of Boston had the highest goalkeeper rating of 8.88 points. But Hope Solo did well at 8.56 points, Ashlyn Harris was at 8.44. Alyssa Naeher was at 8.28. An average goalie would get 7.42 points for the league average of 1.33 goals against average, and the 75% save percent. So, the goalie stat worked okay by itself. However, the goalie average was not near 5 as I wanted.

## 4. Bonus

- You show up to work or practice, you get 9 points for “regular” players and only 1 point for

goalies to make a perfect overall score of 100. But because of the goalie score favored “average” goalies, the bonus had to be adjusted for the other players in the league.

## “Why 9 and 1? Why not other numbers?”

- The “9 /1” system was chosen after much difficulty. The “9/1” looked good overall, but at first, I could not “prove” it was the best bonus system. However, Boston goalkeeper Libby Stout provided evidence that this was the proper bonus system. She is near average for goalkeepers with a 1.50 Goals Against Average and 75% Save Percentage. Also, she had above average playing time. Therefore, she is a perfect candidate to be close to rank 100 overall as there are about 200 players surveyed. If her rank is about 75, then the bonus system favors goalkeepers. If she is ranked about 125 or lower, then the bonus system favors “regular” players. With the “9/1” bonus system, she is ranked 103. Most players ranked below 100 missed several games of playing time. Therefore, this bonus system appears to be the most equitable as far as comparing goalkeepers with the rest of the league.

## Team comparisons

Please note that when comparing teams, then you can eliminate this statistic along with the “appearance” category. All the players receive the same bonus, therefore, it can be eliminated to help see the differences between teams.

## 5. Possession

## 4 parameters were measured. “Net Aerial Duels Won/Lost” , “Net Take Ons Won/Lost”, Recoveries, and Dispossessions (Total).

- Net Aerial Duels and Net Take Ons are obviously the ones “Wins minus Losses”. You add these categories after you find the net total for each.
- All Recoveries were counted. Both offensive and defensive recoveries are gaining loose balls either way. I treated this as a gain in possession.
- Then subtract Dispossessions.
- Multiply the total by 0.134 (or 13.4%)
- There was the “standard fudge factor” of 4.5 added for this category.
## Source Note – for some information about possession data, see http://www.mlssoccer.com/post/2013/03/12/central-winger-possession-trends-and-what-they-say-about-major-league-soccer

## 6. Attacking Passes

## 5 parameters were studied here.

- Key passes is a pass that results in a shot on goal. Worth 1 point as it perfectly fits my definition of “1 point” in this system.
- “Big chances”- are also worth a point each. It is similar to “key passes” from a possession point of view.
- Through balls completed are calculated by through attempted balls per 90 minutes times completion percentage.multiplied by 0.84. A through ball means that you are “splitting the defenders” with a good pass, generally near the penalty box area. This pass has a higher success rate of leading to a goal or an assist. For through balls corner kicks, and crosses, I used the correlation coefficients in the above data. In this case, it is 0.42 for through balls, and then I doubled it to match the math for what I did for assists earlier.
- Completed Launched balls (or long balls) are multiplied by 0.147. This is slightly better than the “ordinary possession stat” of 0.134. A launched ball by definition is more than 25 yards. So, by completing a long ball, you are not only maintaining possession, you are also gaining field position.
- Crosses are multiplied by 0.93 This type of pass has a high success rate leading to an assist or goal. Like through balls, I doubled the above correlation coefficient for convenience.
- Add up the 5 parameters and then add the 4.5 fudge factor to bring the average to near 5.

## Note:

Also, some of the articles listed in the Sources section below give some statistics on goal success rate using some of these parameters. The numbers I have are in the range of some of these sources.

## 7. Passing completions overall

- Overall passing completion league average was 75%. Multiply by 5. This became the fudge factor of 3.75 for this category alone. I chose this number as the average player completes about 25 passes per game and made the league average for this category at 4.92, which is near 5. The 4.5 “standard fudge factor number” would put the overall average at 5.67.
- Calculate Net Completed Passes. Again, you subtract the passes that went awry from the passes that you completed to your teammates. and multiply by a factor of 0.067.

## Notes:

The factor of 0.067 was used as this is the “original possession number”. The 0.134 factor implies that you are “gaining or losing possession”. The 0.067 number implies that you are “keeping possession with minimal effort”. “Ordinary Passes” do have a 75% success rate, and should not be rated as highly than other parameters that have a higher degree of difficulty.

I have always felt that if you keep passing the ball to the wrong team, they will eventually score on you, and that would show up in this category.

## 8. Set Pieces

## 3 parameters studied were free kicks, corner kicks, and throw ins.

- The number of completions for each parameter..
- Free kicks times 34%. According to the data I have there are about 1 goal for every 5.9 free kicks, which is about 17% of the time. Double the number and it becomes 34%.
- Corner kicks times 0.96. I took the correlation of 0.48 from above and doubled the value.
- Throw in times 13.4%. This is the “standard possession score”.
- Add up the above plus Fudge Factor of 4.5.

## Source Notes. For more information concerning set pieces, crosses, corner kicks, etc. see the below: I did not use the data from these per se, but used the information as a rough guide.

- http://thesportjournal.org/article/analysis-of-goal-scoring-patterns-in-the-2012-european-football-championship/
- http://sports.stackexchange.com/questions/977/what-percent-of-corner-kicks-turn-into-goalshttps
- https://www.washingtonpost.com/news/fancy-stats/wp/2015/05/02/the-value-of-a-corner-kick-in-soccer/?utm_term=.dc7871070c62
- //www.pinnacle.com/en/betting-articles/soccer/how-valuable-are-direct-free-kicks

## 9. Goal Scoring Rating

## Overall, add up goals, assists, shots on goal per game, and minus the “missed shots”.

- For scoring, a goal is worth 2 points, and assist and shot on goal are worth 1 point each. A shot on goal is the number of times per game that you made a shot that either scored or required a goalkeeper save.
- A “missed shot” is a shot not on target. However, you cannot tell from the statistic that the player hit the goal post, and almost scored, or whether it was a horrible kick that broke a fan’s pair of glasses 50 rows into the stands. Therefore, I counted this as a simple loss of possession. Therefore you multiply “missed shots” by 0.134. In a sense, I did not want to “reward” players who “shoot randomly”. The overall league average for “shot accuracy” is about 47%. So, if a player shoots twice in a game, and one shot is on target, and the other is not, then the player still gets a score of 0.866. So, the score still favors a typical player. But a player missing shots all of the time will be punished for wasted possession.
- Add 4.5 points to bring the league average up to 5. So, in theory a player not shooting hurts their team slightly, but a person scoring a lot of goals can be worth more than 10 points.
## 10. Defensive skills

## 6 parameters were studied here:

- Interceptions.
- Blocks.
- Clearances.
- Dribbled. Remember this is “bad”, so this is subtracted from the rest.
- Opposing Possessions Disrupted.
- Tackles.

Add them together (and subtract the “dribbled”), and multiply by 0.134. Then add the 4.5 fudge factor.

## Another video to give you a break from reading…

## The results were added up and then placed in a hermetically sealed envelope. No one has seen the results until now. 🙂 As a reminder, here were the players invited to the Rio Olympics in 2016. The number on the left is their total score when all 10 categories were added up. The number on the right is their overall rank of all NWSL players in the first half of 2016.

## Goalkeepers

**Hope Solo 50.6 / 91****Alyssa Naeher**.**52.7 / 71**

## Defenders

**Whitney Engen 56.5 / 14****Julie Johnston 53.0 / 63****Megan Klingenberg 55.4 / 26****Ali Krieger****58.3 / 7****Becky Sauerbrunn 55.9 / 23****Kelly O’Hara 59.1 / 4**

## Midfielders

**Morgan Brian 55.3 / 27****Tobin Heath 56.2 / 18****Lindsey Horan 53.4 / 58****Carli Lloyd 48.6 / 124****Allie Long 57.7 / 9****Megan Rapinoe 0 / 0**

## Forwards

**Alex Morgan 54.4 / 40****Crystal Dunn****56.4 / 16****Christen Press****54.4 / 43****Mallory Pugh 0 / 0**

## Alternates

**Heather O’Reilly 50.6 / 92****Emily Sonnett 53.4 / 57****Ashlyn Harris 54.0 / 49****Sam Mewis 56.6 / 13**

**Mallory Pugh, Carli Lloyd,** and **Megan Rapinoe** scores did not change overall, and their reviews are in Part 1.

## These USWNT players should have been invited to the Rio Olympics.

## Goalkeepers-

**Hope Solo, Alyssa Naeher, and Ashlyn Harris.**- For
**Hope Solo**, she was helped by her attacking and regular passing skills as well as possession score. She would have a higher score, but her yellow card issues hurt her somewhat. - Technically, for the U. S National Team
**Naeher**and**Harris**must have been “coin flips” for who was the alternate and on the main roster. Harris is ranked higher overall, so I would have had Harris in the main group, and Naeher as an alternate. But in “real life,” both are probably about equal.

## Defenders

**Becky Sauerbrunn**falls to rank 23 in the “SNAFU” calculation from number 2 in my “KISS” calculation. The “KISS” calculation (from Part 2) favors defenders, and McHale et al. from Part 1 favors goal scorers. It appears that “SNAFU” is a reasonable compromise between the two prior systems as she was criminally underrated with rank 58 with the McHale et al. system.**Kelly O’Hara**looked good statistically in both prior systems. So, it is no surprise here as well.**Ali Krieger**is a top 15 defender, passer, and set piece player.**Julie Johnston**is the number 6 defensive skills using this calculation..**Whitney Engen**is ranked 14 with these calculations. She is number 11 in “possession skills” as well as the number 18 defensive skills player.

## Midfielders/ Forwards

**Allie Long’s**best category is the “team punishment”, where she is ranked number 5.**Sam Mewis**was the number 18 goal scorer on the list. She should have been on the “main list”, and not an alternate.**Tobin Heath**would have been a top 10 player with “SNAFU”, but was hurt by the 2 yellow cards in a game she received against the Washington Spirit.**Morgan Brian**is ranked number 27 on the list. But she would have been number 7 if she played all of the games used in this study.**Crystal Dunn**is still one of the top forwards in this calculation.To be honest, I still think her omission from the 2015 World Cup should have resulted in criminal charges somehow.

## These USWNT Players are maybe’s

## Defenders

**Megan Klingenberg**is one of the best at set pieces, which puts her at number 26 overall. However, she is a poor defender ranked at 71.

## Midfielders/ Forwards

**Lindsey Horan**is helped by the goal scoring calculation immensely by scoring 4 goals early in the season. She is fairly good in most categories, but does hurt the team with yellow cards.**Alex Morgan**is good at attacking passes, and of course, goal scoring. But she is below average in 4 categories.**Christen Press**is like Alex Morgan in that she is exceptional at goal scorer and good at attacking passes. She is not impressive statistically in other categories as well.

## Maybe they should have stayed home?

**Heather O’Reilly**is ranked 92nd in “SNAFU”. None of her numbers are superior. Especially since she is not great at goal scoring, at this point in her career, she is an average player.**Emily Sonnett**.Unfortunately, none of my calculations justify her being on the U.S. National team.

## NWSL Players that should have been considered!

## Defenders

**Casey Short**continuously make these lists. She is ranked number 10 overall. Short has been getting some U.S. National team call-ups recently.**Christie Rampone**is ranked 34 overall, but is the number 16 defender. I know that there are some better defenders out there statistically, but leadership was sorely lacking at the 2016 Rio Olympics. She was definitely needed for that alone. But she is ranked higher than Sonnett, and Johnston. But Johnston did miss time due to injury.**Yael Averbuch**is number 24. Like teammate**Brittany Taylor**(ranked 19th overall), she is not a great defender, but has plenty of overall skills to make the list.**Lauren Barnes**is again top 3, topping the chart at number 1 with 60.1 points. She is not an elite defender, but her passing skills and set piece skills are very good, and she does not get into foul trouble.**Abby Dahlkemper**is ranked 12. She is similar to Averbuch, Taylor, and Barnes statistically.

**Midfielders/Forwards**

**Dani Colaprico**is ranked 8th overall, and is 4th in defensive skills, So, why is she not the center back in Coach Ellis’ 3 defensive back system? After all, Colaprico’s only weakness is “attacking passes”, which is the same flaw that Long has.**Vanessa DiBernardo**is ranked number 5. She was top 10 no matter how I do the math, it is time for her to be on the National Team regularly.**Beverly Yanez**is ranked 20 with “SNAFU” and was ranked 17th with “KISS”.**Lynn Williams**is ranked 36. Again, she is one of the best forwards this year in the NWSL.**Sarah Killion**has 59.2 total points and was ranked 3. She is above average in all categories (except goalkeeping, of course)

## Best Player in NWSL from the Wrong Country

**Kim Little** is again at the top of the list at number 2 with 59.8 points. Maybe we can make Scotland the 52nd state of the U.S. after Canada becomes the 51st? District of Columbia can wait a few more years! I would invite the United Kingdom, but they would just “Brexit” us anyway. (I will apologize to all Canadians and Scots and British people for that terrible joke, but we need all the help we can get in the U.S – and not just in soccer. 🙂

## Conclusions

**The “SNAFU” analytical system appears to be a favorable combination of the McHale et al. and the “KISS” systems.**

McHale et al. favors the attacking player, whereas “KISS” favored the pure defender. With SNAFU, there is an advantage of having more distinct categories to reward players in various ways. Therefore, with this system, the best players are the most versatile and have more complete overall skills This is intuitive in a way. In theory, you can have a great defender with poor passing skills. But if this defender keeps passing to the wrong team, the team will lose eventually. So, this theoretical defender might not show up on the McHale et al. list because they do not score. But might be top 20 on the “KISS” list. But with “SNAFU”, the 2 categories would offset each other, and the player may show up as average.

## Flaws with the McHale et al. and “KISS” systems.

The McHale et al. system was flawed as they were only able to use statistics with low “p-values”. This is, of course, the proper way to run a statistical analysis. However, this limited the amount of viable statistics to determine a player’s net worth. From the simple correlation and regression data above, we should have seen values close to 75% correlation coefficients for the parameters of through balls and crosses as you are attacking near the penalty box. Also note that I used the completed passes on the correlation study. A weak correlation with an obvious goal scoring opportunity means that there is no way that anyone can use “sound statistics” to determine a player’s net worth. The reason why is because defenders who do not attack, such as Becky Sauerbrunn, are vastly underrated in McHale et al. system.

The “KISS” system also has its flaws. The benefit was obviously simplicity, but with its “simpleness”, comes a downside. With “KISS”, the players were mostly ranked in various categories. This is fine for a season-long comparison of players. But, obviously, you cannot use a ranking system for a game comparison between two teams.

## The benefits of “SNAFU”.

Therefore, a system like “SNAFU” is perfect. On one hand, if you want to do a season end comparison of the players, then you change all of the statistics to an “average per 90 minutes”. This gives the player her “typical game” of the season, and would eliminate the unusually good or poor performances that she had.

And for comparing with a game data between two teams, one of the adjustments is that the data is calculated “as is”, and you do not adjust everyone’s statistics to “per 90 minutes”. This inflates the “bench player” in a game case. Imagine if they played for 1 minute and scored a goal, then the “goals per 90 minutes” would be 90!!!

The other adjustments were mentioned before, as there are no “appearance points” and no “bonus points “. So, when you are done, you look at the team average of the players in the game, then the difference in average team scores should show the difference in number of goals of one team over another, at least in theory.

**Upcoming article (Part 4!) will be coming out in the next few days**.

Look for my review of the actual game statistics between Sweden and the USA at the 2016 Rio Olympics. I will have “SNAFU” player analysis to determine which players were the best statistically in the game, and to see if players may hurt their team with their performance.

In the same article I will also briefly post data from a “friendly” played in October between the USA and Switzerland. This will introduce the testing done to see if my theories with “SNAFU” actually work or not.

## Will this statistic work on one game versus a half season worth of data? We shall find out!

## Another video before sources and acknowledgements:

## Acknowledgements:

I want to thank Alfredo Martinez Jr. and everyone at WoSo Stats for making the data in this series possible.

“Thank you” goes to the official statisticians at the NWSL league website.

Also, thanks to McHale, Scarf, and Folker for their published paper in which was the basis for Part 1 of this series.

## Sources:

## I also placed some articles about soccer statistics in the list below. I may or not have referred to these above.

- McHale, Scarf, and Folker: Soccer Player Performance Rating System , 2012 “On the Development of a Soccer Player Performance Rating System for the English Premier League” Interfaces Vol. 42, No. 4, July–August 2012, pp. 339–351 ISSN 0092-2102 (print)—ISSN 1526-551X (online). ©2012 INFORMS
- I have acquired about a half season’s worth of 2016 NWSL data from Alfredo Martinez, who has https://wosostats.wordpress.com and I also took the data from his report here.
- Some data is also taken from the NWSL site
- http://whatis.techtarget.com/definition/SNAFU-situation-normal-all-fed-up
- Here is a quick glossary for some of the terms above: https://www.whoscored.com/Glossary
- http://thesportjournal.org/article/analysis-of-goal-scoring-patterns-in-the-2012-european-football-championship/
- http://sports.stackexchange.com/questions/977/what-percent-of-corner-kicks-turn-into-goalshttps
- //www.pinnacle.com/en/betting-articles/soccer/how-valuable-are-direct-free-kicks
- https://www.washingtonpost.com/news/fancy-stats/wp/2015/05/02/the-value-of-a-corner-kick-in-soccer/?utm_term=.dc7871070c62
- http://soccer.epicsports.com/soccer-glossary.html
- http://www.mlssoccer.com/post/2013/03/12/central-winger-possession-trends-and-what-they-say-about-major-league-soccer