Tough Fantasy Football Decision? Minitab Can Help!

Are you ready for some football? Here at Minitab, we’ve got our fantasy football teams ready to go, just like many of you. But we’ve got a strategic advantage: Minitab Statistical Software! We’re going to show you some ways you can use Minitab to make those tough decisions about who to start and who to sit each week. You’ll see how Minitab can give you the edge you need over your fantasy football competition this season!

Which running back do I start?

Suppose you drafted Andre Johnson and Tom Brady in the first two rounds. Or maybe you went with Peyton Manning and Larry Fitzgerald. Either way you missed out on the top running backs, and now you’re stuck with a handful of average running backs. How should you decide who to start for week 1? In this article we’ll show you an easy way to use Minitab to analyze last year’s statistics to decide which players to start.

There are many statistics we could look at, but let’s keep things simple. For this analysis, we’ll use a handful of stats that are easy to find at espn.com. We realize that the data we’re using is from last season, and that it may not be the best indicator of what will happen this year. But for week 1, it’s all we have, and we should still be able to draw some reasonable conclusions from it. As the season begins, we’ll be sure to include the most recent stats from the current season. Now let’s get to the data!

Convert the data into fantasy football points

For each regular season game last year, we’ll record the number of rushing yards, receiving yards, rushing touchdowns, and receiving touchdowns for the players we want to compare. We’ll use a missing value symbol for the data when a player was injured or had a bye week. This gives us 17 rows of data. But this is fantasy football, so let’s convert all those yards and touchdowns into fantasy points. We’ll use the standard scoring system used in most fantasy football leagues. However, if your league uses custom scoring, you can change the numbers.

Fantasy Rushing Points = (Rushing Yards / 10) + (Rushing Touchdowns * 6)
Fantasy Receiving Points = (Receiving Yards / 10) + (Receiving Touchdowns * 6)

So if a back runs for 100 yards and a touchdown, he gets 16 fantasy points. We’ll calculate his fantasy points for each week last year, and put the rushing points in one column and the receiving points into another.

Adjust the data for the week 1 opponent

Now that we have the fantasy points for each game last year, we could calculate the player’s average number of fantasy points per game, but that won’t help us much. This average tells us how many points he would score against the average NFL team. But we don’t care about the average NFL team. We want to know how he will perform against his week 1 opponent. How do we quantify this?

Let’s say the week 1 opponent is the Washington Redskins. We need to know how the Redskins defense compares to the rest of the NFL. The first thing we do is calculate the average rushing and receiving fantasy points the Redskins gave up per game last year:

(112.4 Rushing Yards per Game / 10) + ( 10 Rushing TDs / 16 games * 6)
= 15 Rushing Points Per Game

(207.3 Passing Yards per Game / 10) + (19 Passing TDs / 16 games * 6)
= 27.9 Passing Points Per Game

Then we compare these numbers to the average number of fantasy points that every NFL defense gave up last year. We calculated these to be 16.7 rushing points per game and 30.2 passing points per game. To account for how the Redskins defense compares to the rest of the NFL, we multiply our running back’s fantasy points by the ratio of the Redskins’ average, divided by the NFL average. You can find a table of these ratios for every NFL team at the bottom of this article. If a defense gives up fewer fantasy points than the NFL average, the ratio will be less than 1, and the running back’s points will go down. If a defense gives up more fantasy points than the NFL average, the ratio will be greater than 1, and the running back’s points will go up.  To see how it works, let’s convert Felix Jones’ numbers for his week 3 game last year to account for the Redskins’ defense that he’ll face in week 1 this year:

Rushing Points: 9 (Week 3 points) * 0.9 (Redskins / NFL) = 8.1 points
Receiving Points: 2 (Week 3 points) * 0.92 (Redskins / NFL) = 1.84 points

We see that Jones’ rushing and receiving points both went down, because on average the Redskins give up fewer fantasy points than the average NFL team. After we do this for each week, we have an entire season’s worth of data that accounts for how Jones will perform against the Redskins’ defense. Then we add the rushing and receiving points together to get a single column for total points. Now we have a column of data that we can compare to other players!

Compare the players

Let’s say we have Justin Forsett, Jerome Harrison, Matt Forte, and Felix Jones on our team, and we need to decide who to start for week 1. Once we have the adjusted fantasy points for all four running backs, we can use Minitab to help us decide who to start. Just be sure that you adjust each player’s numbers for their week 1 opponent, which will be a different team for each player.

We want to compare the means of all four players to see who has the highest amount of fantasy points. To do this, we will use Minitab’s Descriptive Statistics and Main Effects Plot to compare the data.

Harrison and Forte have much higher averages than Jones and Forsett. Based on last year’s data, Forte should score 11.5 points on average against the Lions, and Harrison should score 12.5 points on average against the Bucs. Those are the two running backs we want to start. But what if we need to choose between one of the running backs that have similar averages? And what’s up with Harrison’s standard deviation of 17.2 points? Let’s create a Time Series Plot for a closer look.

Harrison vs. Forte


We can immediately see why Harrison has such a high standard deviation. In weeks 15, 16, and 17 his fantasy points skyrocketed. Harrison averaged 35 carries in those last three games thanks in large part to the absence of Jamal Lewis, who had been Cleveland’s leading rusher until the final weeks of the season. The numbers are even higher because Cleveland plays Tampa Bay in week 1, and the Bucs had one of the worst run defenses in the league last year. Since Lewis is gone from the Browns this year, and rookie running back Montario Hardesty is out with a torn ACL, Harrison will be the starter for week 1. Because Harrison appears to have a higher ceiling than Forte, he should be the running back we start.

Jones vs. Forsett


Now let’s see what’s going on between Felix Jones and Forsett. They don’t appear to have much of a difference, except for weeks 10, 11, and 12. Forsett has the highest values of either running back during that stretch, so we should look back to last season and see what happened during those weeks. Sure enough, Seahawks’ starting running back Julius Jones was injured in week 10 and didn’t play in weeks 11 or 12. When Forsett was given the starting position, he scored more points than Felix Jones did, even after we accounted for the fact that Forsett is going up against the strong 49ers’ defense this week. And even though Julius Jones is still on the Seahawks team, Forsett looks to be the Seahawks’ starting running back for Week 1 this year. On top of that, Felix Jones is still stuck in a time share with Marion Barber in Dallas. Because Forsett was so successful last year as the starter, we should choose Forsett as the running back to start over Felix Jones.

Now this might not be the most thorough analysis we could do to decide who to start in fantasy football, but it’s a quick way to use Minitab to compare multiple players to get an idea of who might be the better player in a given week. Give it a try, and tell us how it works for you. It might just give you the advantage you need to beat the competition!

Defensive ratios for every NFL team

Arizona 0.97 1.05
Atlanta 0.86 1.11
Baltimore 0.74 0.90
Buffalo 1.36 0.78
Carolina 1.08 0.81
Chicago 1.07 1.06
Cincinnati 0.86 0.90
Cleveland 1.20 1.08
Dallas 0.70 0.98
Denver 1.02 0.84
Detroit 1.16 1.32
Green Bay 0.61 1.03
Houston 1.02 0.96
Indianapolis 0.98 0.94
Jacksonville 0.97 1.13
Kansas City 1.34 1.08
Miami 1.05 1.06
Minnesota 0.63 1.05
New England 0.80 1.01
New Orleans 1.16 0.97
NY Giants 1.14 1.10
NY Jets 0.84 0.61
Oakland 1.47 0.88
Philadelphia 0.87 1.05
Pittsburgh 0.70 0.99
San Diego 0.93 0.98
San Francisco 0.85 0.93
Seattle 1.05 1.15
St. Louis 1.36 1.05
Tampa Bay 1.31 1.04
Tennessee 1.00 1.24
Washington 0.90 0.92

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