The Money Ball Forward

It’s hard to not bring up the movie Moneyball when talking about statistics in sports. Prior to the A’s using this strategy, baseball coaches and managers throughout the MLB were using the most insane metrics to build a team. They want to choose players by the way they look, their jaw line, how they look swinging a bat, do they fit the part. 

In soccer, the same level of outdated thinking is used when talking about a “profile” in choosing a player. Often this results in coaches picking big, strong, fast kids. Unfortunately, that almost never works out when they come face-to-face with a team that can really play. 

The concept behind using statistics, is to find a way to garner an efficiency metric, or a calculation to establish the value of a team, or a positional player based on a composite score.

In baseball, the on-base % is king. It’s more important than an error, or the 1x per season crazy defensive play. Getting on base gives you a chance to score, scoring is the point of the game. 

For a forward,  I would liken scoring in a soccer match to a home run in baseball. Yes it’s important, and we should all cheer. But we should also consider successful pass % (on base percentage), fouls (strikeouts), turnovers (outs), etc. 

So what would be want to look at if we are building a team with a Money ball forward. 

Just rememeber, underneath all of the hype and strategy… soccer is a game of winning, scoring more than your opponent. And there is a statistical strategy that can be deployed to increase those odds. 

1. Score more. 
2. Keep the ball longer.
3. Dont give up stupid fouls
4. Dont give up free kicks
5. Pass to your own team
6. Don’t let the other team/player get behind you

The list is so simple that it feels ridiculous even writing. But the truth is, the lack of a “composite score” for each player leaves most coaches making odd decisions about a players effectiveness.

There is a forward from the west coast this year that had 7 goals. Not bad.

But, what happens when he isn’t scoring?

For a coach it can be a tough calculation, you want players that score, but will their potential of scoring cost the team in efficiency?

If this forward will try to score at all costs, does he create turnovers and counter attacks that create chaos and scoring opportunities for the other team?

We have all played with this forward, if he scores he is a hero, but the majority of the time the ball stops with this player. 

The build up looks like… pass, pass, pass, run, pass (to this player) and then its heads down towards goal. This even happens at the pro level, but in the college game its prevalent.  

The forward either scores or loses the ball creating a scoring opportunity. The momentum and build up die with them. Again, if they score everyone is happy, if they don’t the whole team now has to sprint back to defend a counter. 

Remember, not being scored on is just as important as scoring!

Contrast that with a player that scores 40% less, but helps the team keep the ball, creates opportunities through passing quality and lowers the likelihood of a counter attack.

In that case this player scores 5 goals instead of 7, but also isnt creating a tough environment for his team. That’s a huge improvement in a composite scoring system. 

If the way to win is to score more than the opponent, then it is to also a strategy to be scored on less. And a forward can contribute to both the goals for, and the goals against. 

Every time that forward puts their head down to score, they may be creating a big opportunity for the other team to score. 

If this same forward commits stupid fouls, the momentum is lost. Or stupid offensive fouls, the opponent has another chance to score. 

In a perfect world, you would have a player that could score at will while also being efficient, reliable, smart decision making and the rest. But in reality, coaches are choosing players that are on a spectrum of each skill.  

Imagine this, if you had 11 players on the team that played smart, didn’t lose the ball, didn’t commit stupid fouls, didn’t make 50/50 pass attempts with flicks, dummies and the rest… smart players that kept the ball… how different would the game look. And if you had 11 players with these attributes they would also be a lot less likely to get beat defensively, they would be positionally sound and wouldn’t dive in or take risks that put their team in peril. 

How much different would the play look? Smart, efficient play. 

This to me is one of the major missing components of Division 1 soccer. Where coaches are building programs that have the ability to be efficient, vs run and gun chaos that perils most D1 programs. 

Commonly Used Metrics in College Soccer

1. Traditional Statistics

These are the stats most fans already know:

• Goals & Assists: Measures direct offensive contributions.

• Shots & Shots on Target: Indicates offensive pressure.

• Saves & Clean Sheets: Evaluates goalkeeping performance.

While simple, these stats are still essential for tracking performance over a season. However, they are useless in developing a robust scoring system. 

Which just previewed the MLS academy college camp (a camp for top college players), and the attendees were decided in large part based on statistics. 

Forwards are judged on goals and assists mostly. See how this breaks down. If a forward has 8 goals on the year, that is considered very good. Not great, but good. If 5 of those goals come from PKs that were generated by another player… now we are down to 3 goals, which isn’t that impressive. 

This is the problem with basic stats, they don’t tell a picture and some players that are wanting to exploit stats for a larger goal can demand the goals via PK as one example. 

What these stats don’t show is what happens when the kid isn’t scoring.  Can he play? How often does he foul or get fouled? How often does he loose the ball, go out of bounds, turn over or create the opponent’s next counter attack. Being really good 10% of the time is a huge number, but the other 90% should be statistically important in determining a composite value. When the player isn’t scoring, do they create value or chaos? Do they create a rhythm, or does the rhythm stop when they get the ball? These concepts matter greatly. 

2. Advanced Analytics

College soccer programs are increasingly adopting metrics borrowed from professional leagues:

• Expected Goals (xG): Measures the quality of chances created and conceded. A higher xG indicates better offensive opportunities.

• Passing Networks: Shows how effectively players connect during build-up play.

• Pressing Efficiency: Tracks how often a team wins the ball back in high-pressure areas.

• Possession Metrics: Helps teams understand control and territorial dominance.

These advanced metrics allow coaches to pinpoint strengths and weaknesses in ways traditional stats cannot.

3. Player-Specific Data

Some programs use wearable GPS trackers or performance software to monitor:

• Distance covered

• Sprint frequency

• Work rate in offensive vs. defensive phases

This data helps coaches tailor training, prevent injuries, and improve tactical execution.

Just as important as the data above, we should also be laser-focused on:

• Pass completion
• Tackles won
• Fouls awarded
• Fouls given
• Turnovers
• % of total success/failure
• Air battles won/lost

4. Developing a composite score

Just as we discussed with a forward, goals aren’t the only stat that matters. An average team is scoring 27-40 goals per season. If you look at the number of goals per player, here is a national rank. 

This means in all of college soccer, there are only 15 kids scoring 10 or more goals. So the team has to work. The team can’t rely on one player so there needs to be a composite scoring philosophy that considers total goals (huge +) against other contributions made when they aren’t scoring the other 99% of the game. 

Composite score for Centerbacks

Center backs have been traditionally judged by how well they do defensively only. Are they big, strong, and fast. Do they crush tackles, big in the air, and chase forwards down. 

What this doesn’t consider is what is happening when they aren’t putting on crushing tackles, where the ball goes when they win it in the air, how many fouls do they get with big tackles, how often they dive in which creates the need for a chase down. 

Here are a few scenarios. 

1. Centerback is out of position, but because of speed and athleticism, is able to track down a player (the whole team has to sprint back too), and it ends in a big tackle, but a foul for a free kick. Sure, he is fast and made it look dominating. Here is an alternative:  Centerback has good positioning, steps in front, and wins the ball. Non incident. Or, has good positioning, doesn’t dive in… forward is forced to play back. Non incident. 
2. Long ball is played from the opposing team, centerback goes up for a big header, that goes directly to the other team. + for winning the header, but 2x- for giving it back to the other team. Now the other team is transitioning fast, and the team is in jeopardy. 
3. The thing I see most often is a big defensive tackle followed by a turnover. A CB goes in hard, makes a big tackle, wins the ball, and then passes to the other team. It seems like this is a neutral outcome, but the reality is that this has become a net negative outcome. If a CB is goal side of the forward, steps up to win the ball, then looses the ball… the forward is now behind for a high-pressure transition. This is a nightmare for defense.  All things considered, the better alternative is not winning the ball, but keeping the forward from getting behind, pushing them back. 

How Coaches Use Statistics in Game Planning

College soccer is highly competitive, and marginal advantages matter. Statistics allow coaches to:

• Scout opponents efficiently: Identify defensive vulnerabilities or attacking patterns.

• Optimize lineups: Use data to determine which players complement each other best.

• Adjust tactics mid-game: For example, if stats show a team is weak under high press, a coach might instruct players to exploit that.

Challenges with Using Data in College Soccer

While statistics are valuable, college programs face some challenges:

• Limited budgets: Not all schools can afford high-end analytics software or GPS tracking.

• Small sample sizes: Fewer games per season can make some stats less reliable.

• Data interpretation: Coaches and players need to understand metrics in context — raw numbers without tactical insight can be misleading.

The Future of Analytics in College Soccer

As technology becomes more accessible, expect data to play an even larger role in men’s college soccer:

• Video analysis software integrated with stats for detailed scouting.

• AI-driven insights predicting player performance or injury risk.

• Enhanced fan experience through advanced stats in broadcasts and social media.

The teams that embrace analytics effectively will likely have a competitive edge in recruiting, match preparation, and in-game decision-making.

Conclusion

Statistics are no longer just optional in men’s college soccer — they are a key part of modern coaching, player development, and analysis. From traditional metrics to advanced analytics, the teams that understand and use data effectively can turn numbers into wins.

Whether you’re a coach, a player, or an engaged fan, understanding statistics can deepen your appreciation of the game and reveal insights that might otherwise go unnoticed.

Although stats aren’t great, like they are in baseball… we should be able to analyze the game to determine a semi-objective composite score for a player. Considering things they do well, against things that cause chaos.