Brew Plop week 8 recap

Can't remember that Mondays are Mondays if I have them off.

Here's the laziest entry I've done to date, it's just another look at the running best and worst weekly scores:







I'm glad that the Dunbars managed to have the best and worst Runs. I'm also glad that the Elbows managed 16 HR in week 7, but only 19 Runs in week 8.

I'm less glad for the 5 IP Hops vs Edit matchup that produced a 0.000 tie in ERA. With that matchup, we can officially start discussing fixes for next season. The goal will be to value SP and RP equally. Thinking we'll have to have a min IP and probably change Hits Allowed to Batting average against. Feel free to discuss below.

Rotating league name week 8 recap

I was busy Memorial day weekend and though the public has spoken, Shane hasn't come up with anything I can put up here.

Here's your lazy Tuesday morning update of the best and worst of us:



Since I last updated this after week 5, 15 of 24 possible achievements have been met or exceeded. Notably, every hitting category best has been claimed in the last two weeks. Perhaps my personal favorites are Jay Hey setting the worst BAA record in week 7, then rebounding to set the best in week 8 and A-Fraud setting the worst for ERA while his hitters set the best OBP. I can only assume that his hitters were also facing his pitchers (and refuse to do anything to back up that claim like look at the week's box score).

On an entirely different note, A-Rod gets a lot of flack on a regular basis (often rightly so), however this weekend he showed genuine class after lining a double off Indians P David Huff's head. There are many more important events than occasionally showing class, but I felt compelled to remark on this particular incident.

Rotating League Name Week 7 recap

Since Shane wasn't the big winner this week, I decided to not keep the running tally of weekly bests and worsts. Ever. For this week. And last week.



What you see here is how a team can expect to finish a week given their past performance in each category. What I did was figure out how many weeks (out of 7) a team won, lost, or tied a particular category, then figured out your winning percentage for that category alone (using the same methods as ESPN, where T= .5W +.5L). What you see below is how each team did in each category. If a team won 60% or more of the time, they can expect to win that category each week. Less than 40% and they can expect to lose. Anywhere in between and it's a toss up from week to week.


I used this green color because it's very ugly.

Of note is that there are 4 categories that have been won 100% of the time. Hunt's Wins and Bartha's BB, SV, and K/9. Given this, it is a surprise that Bartha is not doing even better. Also, there's only 1 team that hasn't won a category and that's Corley's RBI, or rather, his lack there of. There's a whole lot of ugly for some people and it's never good when you have several "assumed loss" categories but few "assumed win" to go with it. The take away from this is that you can probably predict at the beginning of a week which categories a team will take and which are toss ups for that particular week.

Speaking of predictions, last week I used some method I don't remember (I think using YTD stats) to predict the outcome of each matchup. Here's how that went:



Well the prediction machine (notice how they are no longer my predictions, but the machine's) had a decent go of it. Only 1 upset (based on overall standings) was predicted. That matchup ended in a tie. Of the other 5 where the computer chalked, 3 were wins and 2 were upsets. As one of the upsets had 3 ties (and actually had the correct number of wins for Streeter's team), and the other upset was my team winning instead of losing, I'm fairly happy with the results. As expected, the results were only barely better than a coin flip.

Let me know if there's some kind of breakdown you want to do and I'll be happy to post it. Barring that, feel free to send along ideas for me to do.

Far too busy reading Lost forums (and doing work) at work this morning. I'll post something later. Unless I'm dead, or have some other malady.

Update: Eating or walking at lunch is for losers.

Back to the old category wins (by popular request according to the survey).
So here's the skinny, I calculated the number of weeks out of 7 a team won (or tied) a category. I then calculated the winning percentage for that category, using the same method as ESPN for our standings (Ties count as half a win and half a loss). Then, I figured out which categories you've locked down for Wins (60% or over), which you stumble through for Losses (40% or less), with the middle counting for tossups. Those numbers proved rather arbitrary, but if you said a team had to have 6 or 7 out of 7 (or lost the same), this analysis doesn't do all that much. Anyway, I took the number you bomb and the number you nail and calculated a win percentage, which doesn't mean much since there are a lot of tossups. The actual number of expected W or L is more indicative of success (as seen by Jon's 6 expected wins every week and his brother's 1).



I assume you can figure out which team is which (it made it a lot easier to calculate using Excel, so you're stuck with it).

For full results on each team/category see below.


I'll probably go through these in more detail in a video recap (assuming it happens this week). Of note, the only sure thing (100% or 0%) on the hitting side thus far is the Dunbars winning SB. I'm a little sad that the schedule thus far has already taken out any potential 100% v 100% matchups. Also, since thus far 3-5 people are using the same pitching strategy, Bobby doesn't have 100% in 3 categories thus far this year. Thus far.

Update: This is a video that you can watch with your eyes.

Rotating League Name week 6 recap

Chapter 6: In which I crap my pants

Whenever I listen to audio books, I always want the old Brit speaking to say that title. Anyway, here's stuff you might care about.

Decided to see how well overall performance can predict weekly performance in a given matchup, so here's a picture of what I found:




The numbers shown for each week 6 matchup shows how many categories were won by the team with the better overall numbers through week 5. That is, if team A had more RBI year-to-date than team B, and team A beat team B in RBI, that got scored a 1 or W for prediction. If either the YTD or matchup ended in a tie, it was scored a .5 (used the same rules as our standings as ties counting half a win and half a loss).

Of note is the Kid-Pawn matchup where every single hitting category went against the past performance (4-2 Bartha, instead of 2-4). At the same time, every pitching category in that matchup went exactly according to the past (though Bartha almost blew it trying to chase Saves history - which incidentally was the only best/worst change from last week, so I'm not bothering to update it this week).

At the bottom of the picture are measures of how well YTD predicted this week's outcome. I actually ran the figures using the current data (through week 6) first because: 1) I thought it would be easier and 2) I forgot that I had the cumulative stats through week 5 handy in the same spreadsheet.

In general, the predictive power based on this one small subset was somewhat better than just flat out guessing (.500). As you might expect (or maybe not, who am I to know what you expect?), adding this week's cumulative stats produces better overall guess, but not that much better than before. The pitching categories seem to follow form a little closer, and so are more predictive (the hitting categories were dead even). I believe this is partly because the league is set up with extreme hitting strategies (this year at least) and because the presence of several rate-based categories means that one good or bad week is more mitigated than in the counting stats. Indeed, when I separated out the rate categories, they were correct 2/3 of the time, versus around 52% each for the positive and negative (and net) counters.

So, after showing that the predictive power is potentially a problem (I mean limited, but I was on a roll), I decided the best way to use this information would be to make predictions for the next week. Here that is:


Yes, I'm going out on a limb and saying that these scores will be closer to correct overall than just picking 6-6-0 for each matchup. Depending on whether this is true, there may or may not be an update on this next week.

My bold predictions (or those of this system I'm using if they are far off) include 4 out of 6 matchups ending 5-7. Shown in gray, there's only one predicted upset (Rod Beck's Counting). If nothing else, this little exercise allows me time to sit around thinking of (good?) ways to combine team names.