Innovation Session: My Fantasy Football Auction Draft – Retrospective Part 2

This is the third post in a series about my fantasy football auction strategy.

I first wrote about the strategy here.

Then after the season I started with part 1 of the retrospective.

In this final post I have a few other thoughts that I’ll share.

The Importance of the Draft

Below are the positions each team ended in and the difference between their expected and actual results.


We can see that in general, teams that predicted well on draft day performed well in the season. The r squared for the linear trend line here is .63 which means that for my seasons we can contribute about 63% of the final results to draft performance.

What accounts for the other 37%?

I would simplify the decision types in fantasy football by describing three.

  1. Draft day – who you predict will perform well
  2. In-season transactions; waiver wire, free agency & trades – the adjustments you make to your team
  3. Coaching – who you start and sit each week

Improper Optimization

Looking back at my draft I realize that I optimized for a scenario where the draft accounted for >90% of the final results. I assumed a greater level of predictability than was possible and minimized the importance of in-season transactions. I also created a coaching nightmare for myself.

In Season Transactions

Thinking about in-season transactions, it is important to understand how many players went undrafted but ended up being extremely valuable. In fact of the $1,945 of actual skill player value my league saw, only $1,553 was drafted. About 75%.

Knowing this we can see that trying to draft a team with a lot of mid level talent can actually be detrimental. Lets think back to my scenario where some teams drafted 10s & 1s, while I tried to draft only 6s & 7s. We now know that 25% of the value of the season had yet to be discovered. Many of those were 5-7s though Knowshon Moreno was arguably a 9. On my team of 6s & 7s I had a hard time acting on the emerging talent because it meant giving up an equal player from my team. Another teams with some 1s however would have easily given up those duds for a mid level prospect.

The pre-draft % of money spent I had allocated to starters was 90% of my budget. Next year I’m going to consider spending all of my money on 70-80% of my team, knowing that I’ll have a few duds in there for the first weeks, hopefully to be quickly dropped for emerging talent.

Final Thoughts

All of the above analysis optimized for regular season performance. Unfortunately, fantasy football, like real football isn’t all about the regular season. On Sunday we saw how a team with a great regular season performance could blow it in the championships. The same happened in our fantasy league. The team that crushed it all season lost in the finals.

So as I think about a strategy for next season, that is one more factor to take into account.

Innovation Session: My Fantasy Football Auction Draft – Retrospective

Last August, before fantasy football season started, I came up with a strategy to approach the auction draft my work league was having. My season is now over, I didn’t make the playoffs, so I want to take a look back at that strategy to see what I can learn. The important part of this analysis is figuring out how much of my poor performance I should attribute to my draft strategy and how much to other factors.

Streaming Defenses & Kickers

The first thing I want to evaluate is my decision to ignore defenses & kickers in the draft. I instead focused my draft strategy around skill players; QB, RB, WR & TE. I did this because I planned to employ a method called streaming. This involves picking up and playing people based on their match-up each week instead of relying on one team.

Over the 13 week season I played 8 different defenses. My total score for defenses was 281.6, an average of 21.6 points per week. (FYI – We have crazy defensive scoring) That would be the #10 ranking defense in the league – after accounting for bye weeks. The #2 defense averaged 24.97 points per week though, so the difference is negligible.

Going forward, I’ll continue to stream defenses. As I get better at it I might even be able to beat single team scores, I noticed that most weeks one of the top three highest scoring teams was available. The maximum possible streaming score was much higher than the maximum possible single team score.

Over the same 13 weeks I played 6 kickers. My total score was 85 which would be the #28 ranking kicker. There were only 6 that did worse. This clearly wasn’t the right way to go. However, the difference between my score and the first place kicker was 4.5 points per week so this isn’t horrible, but certainly didn’t help.

I think next year, I’ll avoid streaming and instead focus on grabbing a kicker from a high scoring team and sticking with them. That seems to pay off more and offer more predictability.

Auction Draft Strategy

Now that I have some insight into my decision about drafting for the D & K positions I can evaluate my approach to drafting skill players.

The strategy I employed was to optimize for value. I wanted to get the most points for each fake dollar I spent. I based this strategy on the idea that players are interchangeable commodities and that it was better to avoid hyped and price inflated stars and to instead grab people that were undervalued. I had hoped that most teams would draft a few stars and then only have enough money remaining to scrape the bottom of the barrel, leaving me to grab a team full of upper-middle tier players.

To state this mathematically, I thought most teams would consist of 10s & 1s, for an average around 5-6, while I’d have a lot of 6s & 7s, for an average of 6-7, thus a slightly better team overall.

Predicting Production

To figure out value I used some projections that football experts had put together about how many points each player would score during the season.

After the draft I evaluated my team based on the projections I used. I compared what was paid in the auction with the pre-draft valuations I was working off of. It looked like I had crushed it. In the table below, positive numbers represent overpaying and negative numbers represent underpaying.


I had gotten 25% (~$50 of $200) more team than I should have and if my numbers were right that should have resulted in an awesome team.

The major flaw in this system is that it is heavily dependent on the accuracy of the projections I used. In a world where those were mostly correct, I would have fared well. Reality however is very hard to predict.

Actual Production

Now that the regular season is over and I have actual point values, I reevaluated the draft using the actual production of players rather than their projected stats. The second column compares the price paid by each team with their actual season production.


It turns out that my draft was basically a waste – $150 of the $200 fake money I spent did not help me. I of course generally knew this, having suffered through the season, but it is interesting to see the exact figures.

What Went Wrong?

I would love a simple explanation.  To be able to say that based on those results, my draft strategy didn’t work and I should not try it again. Unfortunately, a single season of football doesn’t have enough data to evaluate a strategy to an acceptable level of significance.

Fantasy football has the rare ability combine a low sample size with high variability. Football is contact sport that creates often unpredictable injuries & this season was particularly injury heavy, especially for high profile skill players. Injuries can and do happen to anyone.

Thinking About Risk

This leaves me speculating whether the players I drafted were available for value because people knew they would get injured or if I just got a run of bad luck.

Qualitatively I can see in retrospect that I had a risk heavy team:

  • Rob Gronkowski – Started the season injured and missed the first half
  • Darren McFadden – Has been injured every year of his career
  • Steven Jackson – A RB over the age of 30
  • Roddy White – Missed pre-season games due to an ankle injury
  • Dwayne Bowe – New QB & coach & no pre-season indications of heavy use

Often predictions will take this into account, but the consensus stats I used were only concerned with averages, not standard deviation. In the future I would be wise to account for that as well, and probably to come up with a few additional risk factors to account for. When drafting, risk is a fine thing, as long as it is balanced with some stability.

Risk By Position

As I think about risk, one factor that I have data on now is risk by position. We can take a look at this season and see how each position played out.

The chart below shows skill positions and how the drafted players in my league on average lived up to pre-season expectations.


We can see that RBs & QBs really dropped the ball this year. Of course, this won’t come as any surprise to anyone that drafted CJ Spiller, Ray Rice, Doug Martin, Trent Richardson or Arian Foster. In fact, Matt Forte & Demarco Murray were the only RBs, of the 24 with the highest draft price in my league, that performed better than their draft price would indicate.

Meanwhile TE was a gold mine. On average every dollar spent there paid handsomely. Especially Jimmy Graham.

I need a few more seasons of data to see if these are conclusive results. But, common fantasy football knowledge defends that RBs are more injury prone though which turned out to be true this year. If that remains true, what it means for future seasons is that drafting high on WRs will have more lasting value on average. RBs will always be available on the waiver wire as the season progresses and injuries occur.

Risk by Team

I also plotted the difference in actual to expected performance by team to see if there were any trends there.


The two that panned out the worst were Buffalo & Atlanta. Buffalo because of CJ Spiller who was the only drafted player. Atlanta because of the complete meltdown that team saw including injuries of Roddy White, Julio Jones & Steven Jackson (which resulted in a poor season for Matt Ryan).

From what I can tell, looking at a player’s team isn’t a great indicator of success. Neither the team’s win/loss record or their improvement over expectations seems strongly correlated with the success of the fantasy players on the team. Examples include Denver who was an early favorite, but has done even better than expected, Cleveland who is 4-8, but has had a few players pan out, and finally Kansas City who has turned their team from #32 to #2, but not to the benefit of many fantasy owners.

Methodology Criticisms

The methodology I used to do this analysis has some flaws that are worth noting. I made the trade off of using a less rich analysis so that I could complete it faster.

The figures I analyzed are based off of the total points a player scored during the season. I have not taken into account the week to week scores. This creates a few edge cases and masks the true value of certain individuals. For example, players that performed well up until an injury will appear undervalued and players that were inconsistent might appear overvalued.

Thinking about injuries, Aaron Rodgers for example had a great season through week 8. He sustained an injury in week 9 and hasn’t played since. In reality he would have been a fine QB with high value during the first 8 games. I have yet to take this into account and it is worth noting in future analysis.

Looking at consistency, a player like Dwane Bowe who was likely overvalued in my analysis. In Bowe’s first five weeks he scored; 3, 11.6, 0.4, 11.8, 3.5. That level of variance was incredibly frustrating for a fantasy player. In my analysis I would have valued him the same as a player that got 6 points every week. In reality they are much different. In the future I would like to take this into account. (This might value RBs a bit higher as they tend to be more consistent where as WRs can see high variance due to big plays and TDs.

This year I based my projected values strictly off of projected points, optimizing for maximum points throughout the season. Knowing what I know now, next year I want to incorporate more with consistency ratings. I have realized that the worst thing is not a low scoring player, but a player who brings high and low scores at unpredictable times.

Continue reading my analysis in part 2 here.

Innovation Session: My Fantasy Football Auction Draft

This is my second year playing fantasy football and first year doing an auction draft. Being new to the format, I figured this was a perfect chance to study up and walk away with a dominant team.

Here is how I prepared for and executed my fantasy football auction draft.

My League

First off lets talk league scoring so those of you that know what I’m talking about have context. 12 teams. QB, RB, RB, WR, WR, RB/WR/TE, TE, K, D, BN, BN, BN, BN. 13 players each including the 4 bench spots. Touchdowns are 6 points, missed FGs are -1, .1 pt per yard returning, no PPR.

The small bench means we’re going to have an active waiver wire so I wasn’t concerned with grabbing a bunch of sleepers.

Here is the team I ended up with.



I did a few practice drafts and a bit of reading before settling on a strategy. This being the first time anyone in our league had done an auction, I knew I needed to play it by ear and be willing to adjust my strategy on the fly. We could end up with an auction that started out heavy with everyone spending all of their money, or people might be timid and not willing to spend a lot for players right out of the gate.

After a few practice auctions I realized that my success in the draft was going to come down to three factors: the prices I set for myself ahead of time, how I reacted to the draft & how I affected the draft.

Setting Prices

The first thing I learned about auctions is that points don’t matter, delta over baseline does.

I had been thinking about the fact that there was some relationship of points to auction dollars such that if you followed the equation, no matter what players you ended up with, your $200 would get you the same amount of points.

If, hypothetically you could start with a team of 13 $1 players and then upgrade each position – there was some dollar amount for each player at which point spending another dollar on them would not return as much value as spending that dollar on another player. If then you knew what this line was, and were able to find people under it, you can draft a team with a higher point to dollar ratio.

The main problem with all of this is that we don’t know the future outcome ahead of time. Football is especially tough to predict because of the limited sample size, high amount of variance and very real possibility of injury.

I was planning on spending some time building out predictions, but realized that other people had probably already done this. If you read my blog often you’ll know I don’t like doing work that someone else has already done. So I decided to do some research. I found two sources that both seemed interesting.

The first was a spreadsheet posted to r/fantasyfootball. It took the consensus predictions of player performance from a number of experts and applies custom league rules to it in order to figure out where the value line is.

The second was another r/fantasyfootball find called the BeerSheets. The took a similar approach but used slightly different math to account for the fact that players get injured and have bye weeks.

Combining those two data points helped me set my dollar values.

I wanted to make sure I was aware of a few other things as well so on my cheatsheet I highlighted injured players in red and potential hidden gems in yellow. Neither of these would affect my bidding much as the prices had already accounted for that, but it would give me a heads up and save me from relying on my memory.


Reacting To The Draft

Now that I had my prices set and had done a few practice rounds I recognized the importance of being nimble. I didn’t want to end up being upset after the draft that it hadn’t gone how I had planned. The two things I needed the most visibility into was how the draft was lining up to my expectations and how much of each position had been exhausted. I ended up chatting with the creator of the spreadsheet I mentioned above and adding some suggestions – he built out something that was helpful, but I’d already built a version of my own.


This spreadsheet would let me update the value that each player had sold for and the box in the top right would tell me how inflated the prices of those positions were and how much of that position had been exhausted. The idea was simple – there was a fixed amount of fake money in the draft, so if we started off high, we were going to have to come down again. Additionally, if I happened to be in a league where people favored one position, I could see which positions were not inflated and stock up on them, hoping to have trade bait for later.

Here is a quick snapshot of how our draft went. Each image was taken after one round (12 picks).






fantasy-football-auction-draft-seventh-round fantasy-football-auction-draft-eighth-round



You might notice that the draft started off with a 21% inflation. Basically people were paying a lot for star players and it was obvious that no one else had really though about the relationship between dollars and baseline deltas. The first two rounds were so expensive that I didn’t pick up a single player until pick #25.

Looking at the draft over time, here is the inflation of each round.


Notice that the first two rounds were really expensive and then people started to worry about money. That is when I jumped on it and picked up Steven Jackson and Matt Ryan. Even with people bidding me up, round 3 was so timid that I got Matt Ryan for 40%of what Aaron Rodgers went for and nearly half of what Cam Newton did. The closest QB in price was Stafford.

Below are the rounds I drafted players in. You’ll notice I got most of my players in round 6 where the prices were low because people were running out of money, but there were still good players left. Round 7 actually saw a spike because people were spending the last of their money to get the final starters.


Affecting The Draft

The final part of my strategy was to actively skew the draft in my favor. I did this inflating the price of players I didn’t want, starting bidding wars, taking advantage of people that hadn’t read the latest injury reports and strategically depleting budgets for positions where I wanted a value player. Typical aggressive market manipulation strategies.

My basic goal was to make sure that no one got a deal on a player. Any money saved on one player could be used on another. I wanted to make sure that everyone spent the max they were willing to on every WR or RB. Every time they came up I would bid up to my max. Often a bidding war took it much higher, but even if not – it ensured we stayed at or above the value line early on in the draft.

I was willing to take anyone at the right value so when a player was nominated I would wait cautiously to see if they were going to go for a low price. If so I’d quietly take them. If not I’d start mentioned everything good I knew about them and bidding. That usually resulted in a few people paying attention and helping get the price up.

When it came to my nominations, I used them strategically to deplete budget at positions I didn’t plan on spending money at. With my first nomination I chose Colin Kaepernick. I live in SF so there are a lot of 49ers fans in my league. It worked like a charm, he sold for $41.

I then continued to nominate every top 10 QB except Matt Ryan so that everyone would have a starter, hopefully stopping them from bidding on another top QB in case they somehow got stuck with 2 starting QBs. Peyton Manning went for $37 & Matthew Stafford for $20. If you look above at the inflation charts you’ll notice that by the end of round two, 90% of the budget I had estimated for QBs (and 70% of the actual end result) was already spent, meanwhile other positions were at less than 40%.

This strategy worked perfectly – Matt Ryan at $22 was a steal.

Next up in my plan was nominating injured players hoping people didn’t know. I had seen that Le’veon Bell had gotten injured but figured most people wouldn’t know and Yahoo’s system certainly wouldn’t have updated it’s price. Somehow there was a bidding war which resulted in a sale for $14. Turns out he’s going to miss the first month of the season and won’t likely be playing at 100% this year. I made sure to announce that – pretty sure the guy who got them was a bit shaken up. After that point people started to slow their bid anytime anyone mentioned sprained ankles or players missing practice. People were either too nervous to get caught by the same trick or too busy Googling it to see what the deal was.

I tried to nominate Dustin Keller hoping I could pull it off twice but got stuck with him. It was a risk I was willing to take. He’s already been dropped from my team and replaced by a free agent.

Homer defenses were also a big hit. Our league has a few Seattle fans – so Seattle’s defense went for $7 right after SF’s went for $5.


I ended up with a team that has a lot of mediocre talent. It actually doesn’t look that great on paper because I don’t have any stars, but I feel really comfortable with it. The fact that my bench is projected almost the same as my starters means I’m less susceptible to injuries setting my team back and more able to trade. I also have 4 good running backs of which any one could have a breakout year.

Update: I didn’t have a great season, but I think that had more to do with luck than this strategy. You can read my retrospective from after the season here.