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Disagree, I would say the opt out always works out poorly for the team, unless the player opts out for non-rational reasons.
This question provokes my question then:
Why not randomize the 10 BIP also? If I followed the article correctly, you took the first 10 BIP of the season and all randomized 5x5 combinations of it. But could you take a random set of 10 BIP from the original set of 300 <span class="statdef"><a href="http://www.baseballprospectus.com/glossary/index.php?search=PA" onmouseover="doTooltip(event, jpfl_getStat('PA'))" onmouseout="hideTip()">PA</span></a>, and then do the 5x5?
Pretty surprising the number of people who didn't get the joke
It was always going to be this way. The market was begging to become efficient. Its not there yet but to me it is likely it will.
Its pretty simple in my mind: Peer to peer skill based gambling games can only thrive if the stream of people coming in one end is larger or equal to the stream of people leaving. Fish will leave because they are losing, and sharks will in turn leave because there are no fish to profit from anymore. Figuring out how to solve this problem will ultimately save DFS in the long term but I'm skeptical it is even possible.
Maybe I am having a reading comprehension fail, but I don't understand this line: "One caution: <span class="statdef"><a href="http://www.baseballprospectus.com/glossary/index.php?search=DRA" onmouseover="doTooltip(event, jpfl_getStat('DRA'))" onmouseout="hideTip()">DRA</span></a> is not (presently) adjusted for run-scoring across different eras. Rather, it is adjusted to the average runs-allowed by the league for that season." Doesn't the latter accomplish the former?
Wouldn't it be plausible to say that if the service time date is set further in advance thus advancing free agency one year for some players that owners will simply substitute salary away from existing free agents? In other words, the pie will still be the same but just will be distributed slightly differently among the players.
I'm not sure this is what you are saying, but I think an offshoot is that $/WAR needs to change/decrease if you are including front office employees in the mix. Basically, everything needs to add up. If you use the $/WAR derived from free agent players and then apply it to evaluate contracts of front office members you are overestimating the amount of money available in the baseball universe. Lewie, correct me if I am wrong or if this is not what you did.
Obviously your main point of getting more bang for your buck spending on front offices is still valid.
Nitpick: Adam Jones is a right handed hitter.
Nick - any speculation on your part if there was indeed a violation of the "no agent" rule?
I can't tell if Jason likes Buxton or not.
Do the Carlos Gomez and Lincecum ROS projections give credence to the view that PECOTA places too much weight on older data? Have there been backtests done to verify how much older data to consider and how much to weight each year?
Great stuff as always. Maybe my favorite article to read each week.
When doing the math, ideally you should regress Gallardo HR rate (less so) and XBH rate (more so) to some mean - perhaps the mean of pitchers-as-batters.
MGL is mentioning on Tango's blog that you need to consider the impact on the next inning since Gallardo will lead off if pitch to Reynolds and get him out.
It would be safe to say that higher release points correlate with lower than average platoon splits, and vice-versa, correct?
This. Use a collection of very fast runners as an estimate and assume Hamilton is slightly faster than those players.
So in general would you say PECOTA does not work that well with prospects and young players? Having Kris Bryant has the 48th best third baseman prospect and ranking Moustakas over Machado will get you laughed out of rooms.
So there is a bit of a feedback loop when interpreting the results, right. The top of the table is full of high contact rate hitters. Does Marco Scutaro have a high contact rate because of his intrinsic ability or because he sees a high percentage of fastballs (which are easier to make contact on than other pitches)? Both, right?
The first sentence should be reversed. Nava hits RHP better and Gomes LHP.
Count me shocked if the Orioles score the least amount of runs in the divison
Yes but that doesn't answer my question.
Do you have an explanation as to why or why not this phenomenon exists in other professions? I find it curious that this pay structure also exists in say news reporting or acting, but not in say banking and finance. In either case, there is high supply of labor and top positions (GM for a baseball club) are very highly paid, but entry level pay differs drastically in the different fields. Do you think it just has to do with the demand of labor - meaning that there are finite jobs in baseball and media but maybe not necessarily so in finance. Or perhaps that there are very little non-monetary benefits of working in finance.
In regards to the pitcher extra strikes/balls analysis (Tom Glavine example) - I'm trying to grasp how this should be added to their value. In a sense it already at least partially accounted for in strikeouts, walks, and balls in play, right?
Which brings me to a larger point. Since all runs are accounted for somewhere on the field, if we were to add pitch framing to WARP calcs, we would have to subtract it from someone else, no?