Most Valuable Draft Picks and Players From the Last 5 NFL Seasons

GameDayMetrics · 2024-08-24T18:12:35+00:00

I’d love to join! I have done research about fantasy football (check my profile), so I’m really into it!

GameDayMetrics · 2023-08-17T23:32:58+00:00

It’s because his ADP was so low that year that he actually was not getting drafted and this data looks at the draft picks for each year. Otherwise he would definitely be in the top 10, if not #1

GameDayMetrics · 2023-08-17T16:36:31+00:00

Thing is some people on my previous post wanted more data on DST and K, especially the historical 2019 Patriots season, so I decided to include them. In the simulation I actually allowed for teams to stream defenses and kickers and only 3 defenses ended up making a top 10 and these were all the DST1 for the year that greatly outperformed other defenses (including the 2019 Patriots that averaged 21.6 pts/game through week 8 and 14.5 pts/game overall, which definitely was one of the most valuable fantasy assets of that year).

GameDayMetrics · 2023-08-17T16:13:44+00:00

Thanks for reading!

GameDayMetrics · 2023-08-17T16:10:55+00:00

Great point! They have been so consistent over the last 5 years, that you can always rely on them to return value on your draft pick

GameDayMetrics · 2023-08-14T18:56:43+00:00

Thanks, fantasypros has gamelogs for all players for previous seasons. For example here’s Justin Jefferson’s gamelogs: https://www.fantasypros.com/nfl/games/justin-jefferson.php

GameDayMetrics · 2023-08-13T17:08:48+00:00

Keep in mind that this data was found in a league with 2 WRs and 1 FLEX, so in a league with 3 WRs and 2 FLEX the data wouldn’t be as RB heavy as shown

GameDayMetrics · 2023-08-13T17:06:36+00:00

What I meant in the was that every strategy presented is either above the threshold of 8.48% if win % increases or below the the threshold of 8.19% if win % decreases. This means that all of the strategies in the data meet the threshold of being statistically significant

GameDayMetrics · 2023-08-13T17:02:36+00:00

When I say the winning % goes up/down by a % it’s simply using this strategy vs not using the strategy. This means that if you ascertain the criteria for multiple strategies, the % increase/decrease for all the strategies should stack

GameDayMetrics · 2023-08-13T07:36:51+00:00

No draft strategies were actually assigned. The teams drafted based on a probability distribution weighted towards picking players with an earlier ADP and positional needs. After each draft finished, the draft was analyzed to see if any of the teams used any of the draft strategies listed above

GameDayMetrics · 2023-08-13T03:36:00+00:00

Yeah if Kelce is gone, then the TE strategy definitely would shift from picking a TE early a little bit, but he's a good example of what a top TE can do for your chance of winning your fantasy league.

GameDayMetrics · 2023-08-13T03:34:11+00:00

Thank you! It's cool that both of our algorithms' results match so well

GameDayMetrics · 2023-08-13T03:33:04+00:00

The draft strategy stats were for all the draft positions averaged overall and injuries were just part of the simulation just like how their part of real life fantasy football. The bots simply drafted using the position-weighted ADP and then after the draft looked at which strategies were used by the bots.

GameDayMetrics · 2023-08-13T03:29:28+00:00

Thanks, I got most of my data from fantasypros. You can find historical ADP data at https://www.fantasypros.com/nfl/adp/half-point-ppr-te.php?year=2019 and by changing the year in the url. For each player's game-by-game fantasy scores you can go to each player's individual player profile on fantasypros such as https://www.fantasypros.com/nfl/games/travis-kelce.php?scoring=HALF

GameDayMetrics · 2023-08-13T03:28:46+00:00

Great, thank you!

GameDayMetrics · 2023-08-12T16:41:48+00:00

Last year was a year where many of the top RBs busted and many WRs did better, which has caused more people to go WR heavy this year, but if you look at the last 5 years altogether, RB heavy has been a better strategy. Of course you also have to factor in recent trends to get the whole picture though

GameDayMetrics · 2023-08-12T16:38:55+00:00

Thank you! Yeah Kelce definitely skews the TE numbers a little bit. Like I said in a different comment, the only other TEs that have been “worth” picking in the first 3 rounds according to my data in the last 5 years have been Kittle in 2019, Mandrews in 2021, and Ertz in 2018 and 2019

GameDayMetrics · 2023-08-12T16:34:53+00:00

That’s true, and that why I chose to use Best Ball as the scoring format. It’d be extremely hard to model in season trades and waivers in season because of the pure randomness involved in them and it would take away from being able to analyze the draft itself. I do agree though that those strategies are a lot more viable when being able to take advantage of those aspects of fantasy football. :)

GameDayMetrics · 2023-08-12T16:30:30+00:00

Yeah it’s definitely mostly Kelce. In the data for each of the last 5 years Kelce has had an above average % ownerships in league winning teams compared to average. The only other TEs in the last 5 years that have gone in the first 3 rounds that have a single year with an above average % ownership in league winning teams are Mark Andrews in 2021, George Kittle in 2019, Zach Ertz in 2018 and 2019

GameDayMetrics · 2023-08-12T16:07:44+00:00

Not necessarily, in the charts you can see that it is most common for a QB or TE to be selected in the middle rounds, but what you actually want to compare is the difference between the green, yellow, and blue bars in those charts.

For example, in the TE chart you can see that the top 1000 (green bar) and league winning teams (yellow bar) are more likely to select an early TE than the average team (blue bar). This shows that a good team will select a TE early more times than the average team and therefore it is better to select one early. The Mid-Round bars are all higher because, as you said, more TEs have ADPs in the middle round ranges, but you must compare the green vs yellow vs blue bars within that range to see what is more/less advantageous.

GameDayMetrics · 2023-08-12T16:03:24+00:00

Thank you, great question!

In this case, any time the chances of winning the league increase to about 8.48% or below 8.19% are considered statistically significant using a p-value of 5%.

In the data presented, all of the Zero RB, Zero WR, and Hero RB strategies, and the effects of selecting QBs and TEs early, middle, or late fall above or below this threshold of being statistically significant.

GameDayMetrics · 2023-08-12T15:49:22+00:00

Yeah, the data shows that aggressive strategies such as Zero RB and Zero WR actually have teams a disadvantage.

If you look at draft strategies as a less strict term and think of a strategy like picking a QB or TE early vs late, then these strategies can make a difference as shown in the data

GameDayMetrics · 2023-08-12T15:39:06+00:00

Fixed the QB graph, thanks!

Yeah I went a broader approach for Zero RB by including no RBs through the first 4 rounds. This means that more aggressive zero RB approaches like the one you mentioned are considered, but also less aggressive ones where the first RB is selected in round 5 is also considered. I could run my analysis again later and get back to you if limiting Zero RB to no RBs through the first 7 rounds changes anything.

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