This is primarily in response to the epic thread where nobody left happy debating the idea of intangibles.

Why do people have to get so angry about this? I don't get it at all.

I am certainly not a sabermagician, and I am likely below average at traditional science and mathematics, but going off of the list that was provided in that post, I will try to provide methods of making the intangible tangible.

    * Second efforts
    * Heart and Determination
    * Work ethic
    * Hustle
    * Being "clutch"
    * Ability to play thru pain
    * Mental toughness
    * Intelligence
    * Ability to be mentally "in the game"
    * Maturity / Immaturity
    * Team chemistry
    * Leadership
    * Comfort
    * Communication
    * Hot/Cold Streaks

Second Efforts
You can’t really measure why a player will choose to put in a secondary effort, the player himself can try to put it into words, but it can’t be numerically represented. However, the results of his secondary efforts surely can be quantified.

For example, following an error one can measure how many bases are taken by the batter runner, or any existing runners; or how often an out is recorded on the same play that an error occurred. So if, for example, the standard average is found to be, following an error, .1 outs are recorded on the same play, and 1.5 additional bases are taken by the runners; you can then determine whether or not the player who committed the error made up for his mistake above or below average. If following an error, Player A bears out an average of .32 outs recorded, and .8 additional bases taken, you can then have some statistical input as to any secondary effort Player A is giving following his own fielding error.

So if the league average is - .1 outs and 1.5 additional bases per error
And Player A has stats of - .32 outs and .8 additional bases per error

Then one could come up with a stat that measure his averages in comparison to the rest of the league, and assign Player A a ‘Second Effort’ statistic. Maybe one could say that Player A has a ‘Post Error Out Conversion Over Replacement’ (PEOCOR) of .31, and a ‘Post Error Bases Allowed Over Replacement’ (PEBAOR) of -.7


Heart and Determination & Work Ethic
I’m putting these together as they seem very much in the same vein. No, Heart, Determination, and Work Ethic are not things that can simply be represented by numbers. However, one can view the results by a player, and therefore come up with a statistical correlation.

Say, for example, that the average number of ground balls seen during fielding practice by an NL starting shortstop is 10, and the average number of swings during BP by NL starting shortstops is also 10. And say that Ryan Theriot averages 17.4 groundballs fielded during practice, and averages 23.8 swings during BP.

So the NL average for a starting shortstop would be – 10 GBs and 10 swings
And the averages for Ryan Theriot would be – 17.4 GBs and 23.8 swings

So a stat can be formulated that will measure the supposed work ethic, or determination, of Ryan Theriot. One could then say that Theriot has a ‘Fielding Work Ethic’ (FWE) of  1.74, and a ‘BP Work Ethic’ (BPWE) of 2.38. These numbers won’t tell you how Theriot feels about his work, but it will tell you what the results of his feelings are.

Hustle
A classic example of an intangible. One cannot quantify what it is that makes a player hustle. Why Pete Rose always seemed to give 110% isn’t something that can be statistically represented. However, again, the fruits of his hustling labor can be measured.

Say, for the sake of argument, that Reed Johnson’s fastest time of getting from the batter’s box to first base is 5 seconds. Then measure how fast he gets from the box to first base on average during a game. Say he averages a time of 5.5 seconds from the box to first.

One could then say that Reed Johnson has a ‘Hustle Percentage’ of .900

The same can be measured for going from first to second, first to third, first to home, second to third, second to home, and third to home (all on non homeruns most likely). You can then further analyze his ‘Hustle Percentage’ during singles, doubles, and triples, whether he was hitting them, or he was a runner already on base.

Then a way to measure the impact on the results of his Hustle Percentage of .900, you could then compare him with other players with similar fastest times (the original 5 seconds), and see how many extra infield hits Reed Johnson accrued, or look for a correlation of XBH in relation to a players Hustle Percentage (HP).

Again, this isn’t to say that it explains what it is that makes a player ‘give it his all’ or ‘leave it all out on the field’, but it is totally plausible to be able to quantify the results of his effort.

Being "Clutch"
I was confused to see this item on the list, seeing as there are existing stats that measure this. Again it cannot be numerical ascertained why a player might be more ‘clutch’ than other, more mortal, men. But it can seen which players perform at a higher level in ‘clutch’ opportunities.

Examples:
BA, OBP, SLG, OPS, OPS+, wOBA with RISP
BA, OBP, SLG, OPS, OPS+, wOBA in 1 Run games
BA, OBP, SLG, OPS, OPS+, wOBA in the 7th inning or later
BA, OBP, SLG, OPS, OPS+, wOBA against a pitcher whom they have performed below career averages against in the past
BA, OBP, SLG, OPS, OPS+, wOBA with an 0-2 count
BA, OBP, SLG, OPS, OPS+, wOBA with a runner on 3rd and less than 2 outs
Percentage of times a player is successful in advancing the runner with nobody out.

ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 with RISP
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 in 1 Run games
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 in the 7th inning or later
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 against a hitter whom has performed above their career averages against in the past
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 with a 3-0 count
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 with a 3-1 count
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 with a 3-2 count
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 with an 0-2 count
ERA, ERA+, WHIP, OPP BA, OPP OPS, K/9, BB/9 with a runner on 3rd and less than 2 outs
Percentage of times an opposing batter is successful in advancing the runner with nobody out.


Ability to Play Thru Pain & Mental Toughness & Ability to be Mentally "In The Game"
I’m grouping these together as they seem extremely closely related. This is where things get a little trickier, as collecting data requires assuming honesty from all related parties. But in the effort of obtaining accurate measurements, it is likely that relying on honesty is the only choice remaining.

After (or before) every game, a player can be surveyed to determine both his mental & emotional state, and his level of physical pain. A player can relay his emotions on a scale of 1-10, 1 being ‘cidal’ (of the ‘sui’ or ‘homi’ varieties), and 10 being ‘capable of cracking the moon with my swing’. And he can also relay his level of pain on a scale of 1-10 as well.

Now, I know some say that you have to take a person’s pain tolerance into account. But the scale would automatically do that. What may feel like a pain level of 2 to Derrek Lee, might be a 6 for me for example. But the amount of pain in relation to what we’re able to cope with, is what is important. It’s like a handicap, if Derrek has a higher pain tolerance than myself, a leg cramp is going to bother him that much less than it would me, despite the fact that if it weren’t for his inherent ability, we’d be in the same amount of pain.

So, if using an Emotional & Mental scale, the average is 5. It can then be calculated, and trends could be identified, of how a player’s performance is altered based his mental or emotional stress level. Say the average deviation from a player’s OPS during a ‘good’ mental and emotional state (say 7-10) is +15%, and the average deviation during a ‘bad’ state (1-3) is -25%, then one could determine such a player to have relatively weak mental strength in regards to allowing his thoughts and feelings affect his level of play. Whereas a player who shows minimal deviation from his average OPS regardless of his Emotional & Mental state, can be said to have very strong constitution in regards to keeping his head in the game.

The same conclusions can be drawn from the pain survey. If Derrek Lee see’s a dip of only -5% when he’s playing with a high level of pain (7-10), then one could likely conclude that Derrek Lee is able to perform at a relatively normal level even when in physical pain, and that it is worthwhile for him to stay in the lineup even if he’s banged up.

Intelligence
Again, I don’t know why this is listed as there is already such a thing as an Intelligence Quotient. However, I shall assume that this means baseball intelligence.

Now this is something that I don’t know if it can be objectively quantified based on analysis of play. I could say that we could count up the number of ‘boneheaded’ plays in relation to the league average, but ‘boneheaded’ is a completely subjective concept. However, it is very likely that it could be done, seeing as errors are often quite subjective as well, so if the Error is an acceptable stat, then it could very well be that a ‘Boneheaded Mistake’ (BM) stat could become accepted.

But just the same as there are tests to measure one’s IQ, there can be an exam to measure baseball IQ. It could involve a written answer portion, a portion where a player stands in a batting cage and tries to identify what type of pitch is being thrown based on the rotation, and whether or not it will end up in the strike zone. A section of the test could be on the field, where it can be seen how a player responds in a rundown situation, or if he correctly breaks to cover the bag on a stolen base attempt or holds his position, etc.

Now, this idea is completely impractical, but it demonstrates that such a thing as baseball IQ can be measured if someone really wanted to go through the painstakingly ridiculous process of administering tests.

Maturity / Immaturity
To me, this feels like something that doesn’t need to be quantified. It is, in and of itself, truly intangible, as it is witnessing a player’s behavior. This is again something that can only be measured via survey, and not by the player himself. However, that does not mean we can’t poll every player and coach the player has shared a clubhouse with and get their opinion of the player’s maturity level. So using the 1-10 scale, it could likely be determined what a player’s ‘Perceived Maturity’ level is, but that’s probably as close as you can get.

Team chemistry & Leadership
This is the one I was looking forward to the most. We may never be able to completely describe why a player has an impact on those around him, but it can certainly be determined whether or not the play by his teammates is actually being affected.

This, once again, would rely heavily on the use of surveys. Say that every player in MLB was polled about every other player in MLB. And that on a scale of 1-10, they decided how ‘good of a clubhouse guy’ such a player is. And then that data was taken, and averages were drawn out to determine a player’s ‘Clubhouse Popularity Quotient'. In the interest of using current BCB rhetoric, I will use DeRosa and Bradley as examples.

Say for the sake of argument that DeRosa yielded a CPQ of 7.8, and Bradley was found to have a CPQ of 3.6. Now, we can take that information and see what kind of effect their presence in a clubhouse had on their teammates performance.

So we look at every single player that DeRosa has ever shared a clubhouse with. And over the course of their careers, all those players (position only) had an average OPS of .764. We can then look at the splits to see how they were effected by DeRosa’s presence in the locker room. So if when DeRosa was a current teammate, that OPS average rose to .798, and when he wasn’t a teammate, that OPS average dropped to .744, we can clearly see that, for whatever reason, when a player shared space with DeRosa, typically their performance got a boost.

So then this data can be used to formulate DeRosa’s ‘Offensive Performance Influence’ as being .034, to go along with his 7.8 CPQ

And say that the stats bared that Bradley had an ‘Offensive Performance Influence’ of -.008, to go along with his 3.6 CPQ.

So you can then try to identify trends with CPQ, seeing if players with a high CPQ tend to yield a higher OPI. And the CPQ can be further used to identify increases in teammates’ Fielding Work Ethic (FWE), BP Work Ethic (BPWE), Hustle Percentage (HP), or Post Error Out Conversion Over Replacement (PEOCOR) and Post Error Bases Allowed Over Replacement (PEBAOR). Thereby concluding exactly what sort of influence a player has on his teammates, and whether or not a popular player truly makes a big of a difference as we fans sometimes think.

Comfort
This seems entirely related to a player’s clubhouse presence. If a player is comfortable, certainly it will show by his teammates’ response to him. And if he isn’t, well what does it matter so long as he is still able to produce, and it doesn’t effect him enough to show up on his mental and emotional stress surveys.

Communication
This seems two fold.

1. Measuring a player’s communication with his teammates.

2. Measuring a player’s communication with his coaches.

The first seems like it would generally be covered, again, by his overall clubhouse presence. I’d imagine that if a player generally didn’t communicate with his teammates, he’d be given a neutral or negative Clubhouse Popularity Quotient.

The second is something that is harder to identify unless you wanted to cross the ludicrous boundary of ‘Words Spoken With Hitting Coach Over Replacement' (WSWHCOR) . So I think I’d acquiesce to the idea that good communication with the coaching staff is likely something that cannot be measured. However, one can calculate how often a player tells the coaching staff he is injured immediately as opposed to a player telling the staff he was injured months prior. But again, it would be so difficult to quantify.

Hot/Cold Streaks
This is another that I just don’t understand how it can be considered an intangible, seeing as hot and cold streaks are so easily identified.

However, explaining how or why such streaks occur is likely far more difficult.

Using previously described stats and surveys, one could probably find some sort of correlation between a players mental and emotional state, his pain level, and/or any positive or negative clubhouse presences, and his level of play on the field.

For example, if a player or players on a team start a really hot streak directly following a trade, perhaps that newly acquired player has a high OPI.

Or maybe when Soriano goes on one of his cold streaks there is some correlation between his performance and his mood, or his BP Work Ethic.

But certainly there are times when streaks occur simply out of randomness, that’s why a player who hits .333 won’t always go 1 for 3 in a game, sometimes he’ll hit .120, and others he’ll with .540, that’s just the way it works.

 

So, I think that most of these 'intangibles' are actually measurable, but the practical application of the majority of these statistics is so absurdly ludicrous that they will likely never be employed. I don't like the sentiment that 'stats are everything, everything else is religion', but at the same time, a lot of what is perceived as immeasurable, is often quite the opposite.

I see the correlation between Koyie Hill starting games, and the subsequent team record, but if there isn't some sort of statistical analysis that shows that the pitchers performed better throwing to him than to Soto, or that the team OPS rose when Koyie started, then it can't really be assumed to be a direct correlation of cause and effect as much as it can be chalked up to simple coincidence. However, I would be one who would insist on riding out that coincidence until it appeared to 'lose it's magic' so to speak, but I wouldn't be one to assume that Koyie is superior to Geo as a result.

Can't we all just get along?