A real-life example

Now, I’m going to move from an experimental example to reality. What I like about my coin-flip simulation is that it illustrates randomness without any additional internal or external variables. The only variable is sample size. But in real life, there are many variables that will have a significant influence on a team’s record beyond solely their ability. Below I’m listing real records of real teams. I think you would agree that the teams in Group One have bad records and the teams in Group Two have good records and that you’d much rather be associated with Group Two.

Group One

Team A: 1-15

Team B: 4-12

Team C: 4-12

Team D: 6-10

Team A’s record is historically bad. Only twelve teams in the NFL (1.1%) have had a season with only 1 or 0 wins in the 16-game schedule era (16-game era began in 1978). Teams with B and C’s records are probably in rebuilding mode and are in the bottom three or four teams in the league. Team D isn’t very good, but maybe with some luck and a few tweaks, they can be expected to rise to the middle of the pack soon.

Group Two

Team E: 11-5

Team F: 11-5

Team G: 10-6

Team H: 10-6

All the teams in Group Two have records that are likely good enough for the playoffs. 11–5 teams in the NFL will go to the playoffs 98% of the time (see notes for detail)* and 10–6 teams go the playoffs 84% of the time.

So, who are these teams? Well — these are not NFL teams. What I have shown you are real 16-game sample sizes of a few Major League Baseball teams from the 2017 season. Below, I’m showing those numbers again, but this time you will see the teams and their results for the entire year.

Group One

Team A: 1-15 Dodgers, 104-58, best record in baseball, National League Champions

Dodgers, 104-58, best record in baseball, National League Champions Team B: 4-12 Astros, 101-61, 2nd best record in AL, World Series Champions

Astros, 101-61, 2nd best record in AL, World Series Champions Team C: 4-12 Diamondbacks, 93-69, 3rd best record in NL, Playoffs

Diamondbacks, 93-69, 3rd best record in NL, Playoffs Team D: 6-10 Red Sox, 93-69, 3rd best record in AL, Playoffs

Group Two

Team E: 11-5 White Sox, 67-95, 2nd worst record in AL, 35 games out of 1st place

White Sox, 67-95, 2nd worst record in AL, 35 games out of 1st place Team F: 11-5 Braves, 72-90, 6th worst record in NL, 25 games out of 1st place

Braves, 72-90, 6th worst record in NL, 25 games out of 1st place Team G: 10-6 Phillies, 66-96, 2nd worst record in NL, 31 games out of 1st place

Phillies, 66-96, 2nd worst record in NL, 31 games out of 1st place Team H: 10-6 Giants, 64-98, worst record in NL, 40 games out of 1st place

Expanding on the 16-game data to show you the full 162-game season demonstrates the impact that a small sample size can have on a team’s record. With the benefit of having the additional information, you would much rather be a fan, player or manager of Group One.

If baseball had the same number of games as the NFL, and these examples happened to be the 16 games played, the Phillies and White Sox would be going to the playoffs. The Astros and Dodgers might be firing their managers instead of facing off in the World Series.

For additional context and more examples, below is a chart showing the eight best and eight worst MLB teams in 2017. Their overall winning percentage, their best 16 games, and their worst 16 games are displayed on the chart. One of the more interesting results from this chart is that the best 16 games of the teams in the bottom of the league are all better than the worst 16 games of the teams at the top of the league.