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Point Spreads, the Three RB Profiles, and Picking Winners

NFL: Jacksonville Jaguars-Minicamp

About a month ago, I did a piece on the importance of attaching yourself to quarterbacks that play not only for good teams, but ones that are also often found in favorable game scripts. It was an eye opening piece that brought many misconceptions to light about “chasing” points that stem from what is often referred to as garbage time.1

I wanted to follow up with a look at how running backs perform in wins and losses and against the spread but while I was idle, Chase Stuart of Football Guys beat me somewhat to the punch. I encourage you to check that piece out first, because it’s a great starter for where this is headed. I still moved on with my exploration because outside of that player breakdown he posted, there’s little overlap going forward.

Before we get into some individual breakdowns, let’s first look at how fantasy relevant backs finished on a weekly level. Leading off, just how important it was to be attached to the winning club.

rb12

 *Adrian Peterson Was PPR RB6 in week 12 which was a Tie

I don’t think that it’s a surprise that backs that are attached to positive game script are more likely to succeed. Winning the game often leads to a more even run to pass ratio and inherently winning the game means that you outscored your opponent, meaning a player on a winning team was more likely to score touchdowns. Backs attached to winning clubs made up two thirds of the best weekly performers and they made up 70 percent of the 96 backs that posted top six weeks during the season (week 17 excluded).

We know that bad teams are capable of producing useful fantasy running backs on a seasonal level, but being on a poor club does lower the probability that you can reach the elite scorers on a weekly level. You can see the difference when we pull up the same figures when looking at backs who were RB13-24 during the season.

rb13

Now things begin to even out at the position depending on outcome. You can be plenty serviceable on a losing team; you just have trouble posting high end production. It’s why a guy like Alfred Morris looked like a middling producer-even though his play was steady from his rookie season- when Washington ran the third most plays last season already trailing by ten or more points. Look at his 2013 PPR game log.

WEEKRkPlayerFanPtsOPPW/L
123Alfred Morris10.4PHIL
220Alfred Morris14GBL
319Alfred Morris13.3DETL
436Alfred Morris7.1OAKW
616Alfred Morris14.1DALL
726Alfred Morris9.5CHIW
812Alfred Morris15.3DENL
913Alfred Morris18.1SDW
1017Alfred Morris13.9MINL
1128Alfred Morris9.3PHIL
1235Alfred Morris5.2SFL
1322Alfred Morris14.3NYGL
1461Alfred Morris3.1KCL
1541Alfred Morris7.6ATLL
1616Alfred Morris14.8DALL

With Washington frequently in poor game scripts and Morris’ lack of involvement in the passing game, his fantasy output was completely neutered. Also, not making excuses, but keep in mind that in two of their wins, Darrell Young and Roy Helu each had three touchdown games, which easily could’ve been Morris’ had the Shanaclan not hated fantasy football. This leads to the next question, which types of backs are the best types to own in fantasy and aren’t affected by the outcomes of games?

Methodology

The first thing I did was take the top 50 backs being selected using current ADP from Fantasy Football Calculator. Then I cut out all of the rookies and backs who have been career backups so far, not timeshare options. So guys like Toby Gerhart, Bernard Pierce and Ben Tate are gone. Then I separated the remaining 37 backs (I added Ben Jarvis-Green Ellis and Ahmad Bradshaw in for good measure in this exercise) into buckets similar to their three draftable profiles  and ran them through the Games Splits App to get their career marks in terms of game outcome and pregame point spreads. Here’s what the profiles broke down to and the total games being used in the samples.

Profile 1

Profile 2

Profile 3

Adrian Peterson

Doug Martin

Reggie Bush

Alfred Morris

Arian Foster

Giovani Bernard

BenJarvus Green-Ellis

LeVeon Bell

C.J. Spiller

Chris Ivory

Chris Johnson

Donald Brown

DeAngelo Williams

Steven Jackson

Pierre Thomas

Eddie Lacy

Matt Forte

Andre Ellington

Marshawn Lynch

Maurice Jones-Drew

Joique Bell

Shonn Greene

Ray Rice

Danny Woodhead

Stevan Ridley

LeSean McCoy

Darren Sproles

Zac Stacy

DeMarco Murray

Lamar Miller

Trent Richardson

LeGarrette Blount

Knowshon Moreno

Frank Gore

Darren McFadden

Ahmad Bradshaw

Jamaal Charles

Ryan Mathews

Fred Jackson

915 Games Played

1067 Games Played

552 Games Played 

I didn’t just randomly pick and choose who went where because it felt right; I used career tallies to decide which bucket the backs went into. Since some of these backs have bounced around profiles in their careers, I made some cutoffs to distinguish them. Backs that averaged  over 12.5 carries per game plus 2.5 receptions went into profile two because 2.5 receptions per game is right at the line of what we want out of our PPR backs. Profile one is made up of backs that fall under the 2.5 reception per game mark and profile three is backs that are over that receiving threshold but don’t reach the requisite rushing attempts per game to be considered in profile one or two. Don’t get hung up on who is exactly where now. Reggie Bush may be a career profile three back that has been a profile two back the past two seasons, but the goal in the end is knowing which profile of back is the safest to draft and applying that to the 2014 group of backs and where they’re currently being drafted. Should you take Danny Woodhead over Jeremy Hill when that time of the draft comes? Is an Alfred Morris type a better option than a guy like C.J. Spiller in the third round? Those are the answers we’re after.

Before peeking at how the separate profiles compared to one another, here’s how the entire group fared in their combined 2,534 games played against the spread and game outcome.

rbwl

The gap in output in wins and losses is significant, especially in rushing output, but in terms of points spreads things get a lot tighter. That’s why we want to find out which profile is the best buy so we can protect ourselves from variance in game script.

Wins and Losses

rbwlp

It’s not a revelation that profile two is the strongest overall because they do everything. When script goes poorly, their receiving prowess still keeps their overall yardage afloat. You can see the jump in receptions for backs when the games fall out of favor an offense.  A takeaway from that profile would be to look at profile two running backs attached to poor teams to get a feel for what you may expect this season. Zero RB Savior Toby Gerhart is on a team that likely has a six win ceiling this season, so the gap between him and a profile one back on a team expected to be in more favorable game script is a lot tighter in terms of average points per game. As far as current ADP goes, Ryan Mathews may be equal to Gerhart at a lesser cost if you believe the Chargers are going to be significantly better than the Jaguars.

A profile three back on a good team is on average better than a profile one back in a bad situation. So while you may be considering the upside of Lamar Miller in Bill Lazor’s offense, Pierre Thomas may be a better player for you to consistently start. I can keep going on, but you get the idea. Sort through ADP and apply the current profile for each back to the 2014 season.

Point Spreads

rbspread

When applying point spreads, you see the gaps draw a lot closer in almost every area. Actual game outcomes are difficult to predict as the spread tightens up. If we could accurately predict them, we wouldn’t be here right now. We’d be sticking it to Sam Rothstein. The same logic applies here as it does to the above table, a profile three back on a team favored on average outperforms that of a profile one back on a team not expected to succeed. For example, on a weekly level, you may play Darren Sproles over Bernard Pierce given the lines for that current game. You don’t even have to play the game of choosing players that cost similar draft capital, you can incorporate this into playing arbitrage throughout the draft as well.

Conclusion

Game script and flow are hard to predict, so it’s normally just avoided. But there are probability patterns we can exploit. Backs on poor teams can definitely still be useful, so you don’t need to set out and avoid them in your drafts. But you can see the impact of what being on a successful team weekly or seasonal can do for the position on a game to game basis.

The individual results used for the samples taken are more scattershot than the quarterback results, which remained very linear across the board no matter who the player. Since I never like to leave you empty handed on seeing what went into the tables, here are two heat maps with the running back performances in wins and losses as the margin of victory and defeat fluctuates. The way you would read it is that these are the totals against the players’ average per game output. So in wins, Adrian Peterson averages three more points above his career average and follow that down the line. The same holds true for lower graph, just with losses. The number under each green and red column is the total number of games that make up that sample, so take that into account.

wheat

 

lheat

 

  1. I prefer to use “off script” instead of “garbage time.”  (back)

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