In the last post, I went over a very useful tool and how to run some calculations to determine probabilities that a particular player will be hurt opening the window for opportunity for the backup on that team and how they added up over a course of the season combined with multiple late round selections to provide upside. But that was a season long format idea.
I am going to take the same logic and apply it to the big fanduel and draft kings formats where you are in a tournament formula.
These tournaments sometimes have 20,000 entrants or more (and some less), and some big tournaments get into hundreds of thousands of entries and some freerolls get into the millions. But if you're playing in these, you have to think outside the box.
Put it this way, if you have the COMMON players and you are right, you are ahead of maybe 2/3rds of the league. But when the total is 20,000, that means there are about 6,700 people you have to compete with at every other position. Contrast that to a pick where 1% of the league has. When we have the top scorer in that position by a wide margin, we only have about 200 other players who have matched our performance at that position, and so we have to both not give the leadback and outscore 200 other players to win. Still difficult, but much more plausible. Of course, you have to weigh this with the idea that the lesser owned players are probably still less likely to score as much... fortunately varience is your friend and the unexpected happens because something has to happen and things aren't totally predictable.
The chances of this longshot player doing well don't nearly need to be as high, but a big question is how high do the odds have to be, how high are they, and what is the optimal decision?
Let's start with a #3 runningback that we think will substantially benefit from injury to either of the first 2 backs on the team and that virtually no one will have on their team (until they read this article or have similar theory). Let's say he's also a kick returner.
So I'm not giving anything away, I'll choose Branden Oliver in another dimension where he did not just tear his achilies and get put on IR (Most of this was written in 2016). We'll spell his name BrendOn Olivor to designate that it's not the real Branden Oliver and that this is hypothetical.
If Woodhead or Melvin Gordon gets hurt, guess who bennefits? Brendon Olivor. So what we want to do is first determine the probability that EITHER Woodhead OR Melvin Gordon gets hurt in a single game.
Melvin's projected 90% risk of injury per season (in 2016) works out to be about a 13.40% chance of injury in an individual game. Woodhead's 16% over the season works out to be 1.09% chance. The chances that neither are hurt are .9891*.866=~.8566 or about 85.66%. This leaves a 14.34% chance of an injury on this particular game. The odds that BRandon Olivor sees significant RB2 action at some point in the game is 14.34%.
Now, of the 14.34% say 1/4th of it happens in the first quarter or about 3.585%. Of that 3.585% chance, we can divide his odds by 32 as there are 32 other team's RBs. That leaves maybe a 0.112% chance that Oliver is the leading back. Possibly a little less because of talent. You think, so what, that's insignificant. But with likely zero percent ownership and an edge if he scores an outlier amount, plus low cost that allows for you to stack a bunch of high probability performers, this may still work out.
Since there is a 1/20,000 chance of winning or 0.005% this isn't necessarily a bad thing IF we can be confident that less than 0.112% of entrants select "Brandon Olivor", or that the combination of the other picks gives us a better than 0.005% chance of winningor making significant cash.
in tournaments.
However, given his cost you might say he only needs to be a top 10 back AND also we need to pick other positions. But before we do that, we also may want to consider volume times production.
One thing you might do is look at the NFL breakdown of yards per touch and TDs per touch. You can simulate this as well as the probability a player will get a certain number of touches. The probability that a #3 RB gets 10 touches and scores 3TD is not zero, so given no injury or given the injury happens very late in the game, we still have a non zero chance of Mr. Olivor being a good selection. He also returns kickoffs which gives us a non-zero chance of a kick return for a TD.
And then you have a possibility for a stack. Putting in the QB for a potential pass catching back or the defense for a potential kick return TD scoring double, one for the defense, the other for Oliver places added significance IF this rare event happens. You are not trying to win every game, you are trying to win a high percentage of games THAT a rare event happens that no one has priced in.
But the work you'll want to do to really optimize this is to run a monte carlo simulation and simulate an entire roster with all possible injuries perhaps broken down by a number of plays per game that you expect and you can determine the probability that you outscore a particular threshold of points to cash and what the payout is given the event...
You'll have to do something to adjust for skill. Afterall, CJ Spiller is probably going to have a better chance at breaking a 99 yard TD than Mike Tolbert. Mike TOlbert is probably a lot more likely to be used near the redzon in goalline situations and will probably have a better chance at driving the pile forward 2 yard TDs. Yet you'll still want to constraint a player by a probability. A probability that there are X number of plays in a game... A probability that he gets the 1st, 2nd, 3rd, 4th, 5th and so on touch of the game. ALL possible numbers of yards that the player could go for on that particular carry constrained by a probability of that event happening. Or perhaps you simply "chunk" a play into groups of potential outcomes. 0-5, 5-10 yards, 10-20, 20-30, 30-50 and 50+ and then randomize exactly how much.
Ultimately, your results are still only as good as your assumptions, but by structuring simulations you can break down a projection into it's simplest part, make assumption about those parts, and then use those assumptions to simulate results and find the roster that yield the best outcome.
Rather than try to estimate ownership and roster, you might instead just determine a rough estimate that 200 points equals some amount of cash and 190 equals a different amount and 220 equals a different amount based upon probability of finishing first, second, third, etc.
More importantly, you can simulate tens of thousands of results based upon these assumptions and approximate your chances of finishing in certain point thresholds and find the players that likely offer a substantial advantage that isn't accounted for.
Of course, this is a ton of work, but there are rewards for doing it of course.
Ultimately what matters is that most players don't consider that talented players like DeAngelo Williams say week 4 when LeVeon Bell is back from suspension still have a significant chance (about 5% in this case) of at least being "the guy" for an entire half of play. If 100 people out of 100,000 are using this player, and his odds of outperforming the points per dollar spent required to win are more than 0.10%, then you can consider playing this player.
The most challenging thing about tournaments is you are not trying to necessarily pick the best lineup, but you are trying to pick the lineup that has a better chance of winning than what is represented by the crowd,... without actually knowing the exact percentages that the crowd will use.
Contrarianism is expensive, meaning the actual results when a player substantially outperforms certainly doesn't even gaurentee a win, and you have to place a lot of losing lineups before you get a single winning lineup. But the payout is there if you can manage your bankroll and risk an extremely small percentage on these massive tournaments.
