I saw where Rynman was working on his thesis, and as a university prof, I decided I'd do everything I can to help him finish that

So, I've taken the 12 team leagues here and put them into excel and started running some numbers. Here's some initial results based on the first 60 teams analyzed. I'm only using the 5x5 stats for this.

Let me explain the presentation. I think the best results will come simply by combining all league results and then ranking the teams within cats. Anybody who has played enough fantasy knows that the league winner will generally capture about 70 percent of the available points. So, in looking at the results, I've identified the following:

The highest amount achieved
The 90th percentile--in general, I think this represents what it would generally take to win this category.
the 75th percentile--in generall, I think this represents what it would take to be competitive in this category. With this level in a category, you'd probably finish 3-5th in the category. And,if you are competitive in all categories, you would be in the hunt to win the league.

However after seeing this and doing my own digging I'm convinced that winning cats isn't as important as knowing what it takes to win the league.

You mentioned that a league winner captures around 70% of the available points, but the number I got (with a smaller sample) was higher. 70% means a 5x5 12 teamer could win with 84 points, which seems low, but that may be a good number in a competitive league. Can you look at your data and come up with the average percentages needed to win?

Can you also find what the average number of cats the league winner did win? Its mathematically possible to win without winning any cats, but probably difficult to do.

Some analysis of the numbers:

Averaging things is out is difficult, since leagues have different numbers of active players. However assuming an average of 9 batters active, we can get averages for each starting player per percentile:

90% 75%
R 109 96
HR 29 25
RBI 107 91
SB 16 14

I think this works reasonably well for batters, but I getting averages for pitchers is more difficult. I find that the number of active pitchers changes a lot, along with the number of RPs needed.

I did notice that it takes a lot of saves to win the cat. That's a bit surprising, but I think it may show that the teams winning saves are really loading up on them and are leaving everyone else in the dust. 130 saves is a lot of saves - 3 40+ closers doesnt quite get you there.

The number of wins it takes to win or be competitive is relatively low. I think that shows 11 and 12 game winning SPs have a lot more value then they're given credit for, as long as you have at least one pitcher to anchor your staff.

Say you get 10 wins from 3 RPs. An average of 12 wins for 7SPs will get you to 94 wins which will gets you to the 90th%. If you only have 6 SPs, and 12 wins from 4 Rps, then 12.5 is need to get to the 75th%.

.......barely makes all of the 75th percentiles, yet I find it hard to imagine it not winning 2/3 cats and being strong in others. Obviously I'm wrong.

Interesting reading. Thanks GMA

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However after seeing this and doing my own digging I'm convinced that winning cats isn't as important as knowing what it takes to win the league.

You mentioned that a league winner captures around 70% of the available points, but the number I got (with a smaller sample) was higher. 70% means a 5x5 12 teamer could win with 84 points, which seems low, but that may be a good number in a competitive league. Can you look at your data and come up with the average percentages needed to win?

Yeah, I'll try to do that. But one thing about presenting it this way (in terms of percentiles) is that it lets you decide how to deal with this. If you think 70% is kind of low, then you can focus on the 80th percentile (I'll post more complete data as I finish it).

swyck wrote:Can you also find what the average number of cats the league winner did win? Its mathematically possible to win without winning any cats, but probably difficult to do.

That'll take longer, since I've effectively stripped that info out by combining the leagues. But, I can try to go back and do that.

swyck wrote:Some analysis of the numbers:

Averaging things is out is difficult, since leagues have different numbers of active players. However assuming an average of 9 batters active, we can get averages for each starting player per percentile:

90% 75% R 109 96 HR 29 25 RBI 107 91 SB 16 14

I think this works reasonably well for batters, but I getting averages for pitchers is more difficult. I find that the number of active pitchers changes a lot, along with the number of RPs needed.

I did notice that it takes a lot of saves to win the cat. That's a bit surprising, but I think it may show that the teams winning saves are really loading up on them and are leaving everyone else in the dust. 130 saves is a lot of saves - 3 40+ closers doesnt quite get you there.

The number of wins it takes to win or be competitive is relatively low. I think that shows 11 and 12 game winning SPs have a lot more value then they're given credit for, as long as you have at least one pitcher to anchor your staff.

Say you get 10 wins from 3 RPs. An average of 12 wins for 7SPs will get you to 94 wins which will gets you to the 90th%. If you only have 6 SPs, and 12 wins from 4 Rps, then 12.5 is need to get to the 75th%.

I think as I add more teams some of this will smooth out a bit. But, I like some of the points you make here.

.......barely makes all of the 75th percentiles, yet I find it hard to imagine it not winning 2/3 cats and being strong in others. Obviously I'm wrong.

Interesting reading. Thanks GMA

Remember that this is combining all 12 team leagues in the thread, so there's variation in games, number of players with stats counted, etc. I think players can look at the percentiles, and then adjust towards their league. If you are in a 12 team league that has high games played limits, then you probably want to look at the 85th or 90th percentile. If you are in a 16 or 20 team league, you might want to look at the 60th percentile.

OK, here's an analysis based on 264 teams (22 12 team leagues). Again the "top" is the best score recorded by any team. The other rows show the value of the stat category for the 90th percentile, 80yh percentile, 75th percentile, 70th percentile, and 60th percentile.

The average line shows the average value of this stat for teams that finished first in their league. Just to be clear, that's not the average of teams that finished first in the category. It's the average of that stat for the teams that won the league.

There's some interesting differences there. The league winner averages are between the 80th and 90th percentile for R, RBIs, and HRs, but between the 75th and 80th percentile for BA, and the 60th and 70th percentile for SBs.

On pitching, the league winner averages between the 80th and 90th percentile, except in WHIP where it's between 75th and 80th.

I wanted to check how these results stack up against my league. My league is a 16 team mixed league (6x6). Over the last 4 years, I've tracked what it takes to finish 5th in each category. that earned you 12 points, and over the 4 years, if you've been around 140 points, you've been in the hunt for the title.

Here's the average offensive stats for the team that finished 5th in the 5 standard categories; I've noted the percentiles from my table above:

This sort of confirms my intuition that you can use these percentiles for most leagues, adjusting up or down based on whether it's AL/NL/Mixed, number of players contributing stats, IP and Games limits etc.

Again, as with the overall numbers, the SB percentile is the lowest. I think this shows that winners tend not to focus too much on SB strength, because that often carries a price in terms of other cats.

For keeper leagues one good use of this is to compare your keeper projections to the values of these stats categories at the percentile you choose. This can show where you need to emphasize in a draft. For example, my numbers show that I'm OK in HRs and SBs and Runs, and weakest in RBIs.