To recap what we’ve covered so far:

- decided we wanted to track our paintball stats
- figured out how to track the raw data
- started storing the data
- created a starting model to rank our players

Given the above, one of our first big questions was “How much does getting the first elimination in a game matter?”. We guessed that it was kind/sort of important but we had no idea how important and therefore couldn’t answer questions like: “Is it better to try and lane someone out or take the risk and have our snake guy make a big break?”

If you’re reading this I’m going to go out on a limb and assume that you know something about paintball. That’s great because to answer the above question we’re about to go all Socratic Method and get you in the frame of mind we were in as we started creating our statistics.

**There are two kinds of statistics, the kind you look up and the kind you make up.
-Rex Stout**

You’re at a paintball field watching two college teams about to start a game. Let’s call them Team A and Team B. You know that the two teams are evenly matched.

First question to you: “Given that the two teams are evenly matched, what are the odds that Team A will win?”

This is not a trick question even though it sometimes stumps people. If you think of it as flipping a coin the answer becomes clear:

**Odds of Team A winning a 5v5**

If the two teams’ skills levels are balanced then Team A should beat Team B 50% of the time. The more observant among you probably realized that we didn’t need a database, Excel spreadsheets and lots of Perl to figure that out which brings us to the next step in our line of questioning.

“If Teams A eliminates a player from Team B, what are Team A’s odds of winning now?”

Aha! Now we are getting into the mysteries of the paintball universe! Over the years I’ve asked a lot of people both in and out of paintball this question. Most people go through the following thought process:

- it has to be more than 50%
- it also has to be less than 100% because I’ve seen teams win 4v5s lots of times
- I would guess somewhere around 60-80%

And you know what, they would be correct!

**Odds of Team A winning a 5v4**

As I mentioned back in the first post in this series, not only was this the first time we knew this, it was the first time anyone knew.

It also kicked off some pretty interesting observations. First of which was: if you are in a 5v5 and you lose a player, your odds of winning get cut almost in **half**. Turns out getting that first elimination was pretty important.

An interesting corollary to that observation was that if you were in 5v4 and your team loses a player, you just almost **doubled** the other team’s chances of winning.

Below is the table showing every combination of players left on either team:

At this point, you might be wondering: “This is good to know but how did you use this to find your best players?”

Good question! The first revelation we had was “Getting the first elimination is huge! Who are our best laners?” followed shortly by “Who is getting out a lot in 5v4s??”

I’ll dedicate a whole post to laning so for now we’ll focus on the 5v4 scenarios.

**Whoever said, ‘It’s not whether you win or lose that counts,’ probably lost.
-Martina Navratilova**

Now, we knew that going from a 5v4 to a 4v4 was not a good thing. We guessed that given the pool of players we had, some of then tended to get out a lot in 5v4s and some didn’t. In other words, we wanted to know who was “good” in 5v4s and who was “bad”.

To do that, we first had to determine how often an average player on an average team gets eliminated. After tracking several matches we determined that number was **8%.** So how did our players stack up to that number? Check out the histogram below.

**Number of players at each percentage odds of being out in a 5v4**

This was pretty shocking! Since we knew the average odds of being eliminated in a 5v4 was 8%, anybody below that we could consider “good” and anyone above “bad” at handling 5v4s. If you add up the numbers you’ll see that out of 21 players, **10** were below average.

“But doesn’t that make sense, Alex? Shouldn’t half of your players be below average?”

Another excellent question!

The 8% number is across **multiple college teams**. To be more specific in plain language: half of our players were not even as good as an

The craziest part about all of this is several of the players on our A line were being eliminated in 5v4s almost **25% of the time!**. In some cases we would have been better off if some of those players didn’t even walk on the field!

If you’ve followed along this far: congratulations! This was the longest post to date. Coming up: How does a college team compare to a pro team?