An Explanation and a Tangent

I've been extremely ineffective as a "blogger", perhaps lending credence to the idea that I am not a "blogger".

I offer up an explanation, if not an excuse. After completing my last free agent regression model, I discovered that trying to perform a regression using all the variables in the dataset is much more complicated than I initially thought. The Excel spreadsheets containing all the data are not only organized very differently from year to year, the same variables have different names. As a result, the effort I have to put into it is much higher than before, or I measure slightly different things. This all coincided with moving and so I lacked an important thing called "motivation". I hope to keep working on this project soon but we'll see.

BUT, in an attempt to give something worth looking at, I provide this:

The Toronto Raptors are very good this year. I don't think they'll go to the NBA Finals, but I also don't think they need too many lucky breaks to get there. And, living in Canada, I get inundated with Raptor commercials every time I watch TSN. The slogan for the Raptors this year is "We The North". Clearly inspired by Game of Thrones, Canada's role as the USA's "neighbor to the North" add to make the slogan more relevant. Except that, does it make perfect sense. At first blush, of course it does. Toronto is in Canada and Canada is more North than America, where every other NBA team is located. But here's the unfortunate(?) reality.

courtesy of http://mapfrappe.com/IsoLonLat.html

There are markers at three NBA cities, Toronto, Minneapolis, and Portland. In terms of straight latitude/longitude, Toronto is actually the third most Northern NBA city. And it used to be the fourth while Seattle was still in the league. Interesting to note (at least for me) is that Portland is the most North NBA city, I definitely thought that it would be Minneapolis.

Now, you could argue that the "We the North" slogan represents all of Canada and that it still fits perfectly because nearly every part of Canada is directly North of a part of the United States, which is valid, I suppose. But it implies that every part of Canada is "Raptor Country" even though most of the country is closer, distance-wise, to a different NBA team. You could take that view, but I think it's more fun to make fun Toronto for their somewhat silly slogan.

Minnesota Timberwolves: "We the MORE NORTH"
Portland Trailblazers: "We the Northernmost!!!"

More Complicated. More Accurate? Salary Projections II

The model becomes nigh unexplainable. The table is impressive. Does that mean it's better?

In my last post, I looked at a simple linear regression for the NHL free agent skaters from this past offseason and saw that model could be created to predict how much money a given skater earned based on some measurables such as Height, Age, Goals Scored, and Penalty Minutes. The model turned out to be somewhat representative but not exactly indicative, meaning that it nailed a couple of players spot on and really only got in the ballpark for some others.

There were a few reasons that this model was "simple".

1) Split up position into defensemen and forwards. The reality is there are multiple forward spots and I suspect that there is a difference in wage earnings between centers and wingers of identical statistics but for simplicity sake, I lumped all forwards together. The cleanest way to account for all "positions" is to measure defensemen and forwards separately.

2) I only looked at 9 statistics as a predictors for a 10th (Average salary). The data set that I culled from contained 121 variables (granted, some were a bit redundant like Age and Date of Birth, but still). I tried to pick out what I thought was most significant but without using the full set, it's subject to my own personal bias.

3) There were no interactions. This is the easiest model to look at. No variable has anything to do with another. But those who follow hockey even a little bit know that teams (or announcers anyway) put a higher value on big players who can score. All other things being equal, teams are more interested in a big bruising player who scored 8 goals than a small player who scored 8 goals. We'll see if that turns out to be true.

The results of the last piece were that I could account for most of the difference in salary between players, but not to any acceptable degree. This round turned out a little bit better.

***Disclaimer that few care about this sort of thing: Once again, the data was not normally distributed. This could be due to a small sample size but I find it unlikely given the sample size of 591 (once outliers were removed). More likely, the data is simply not suited for linear regression, though it is not yet known if it's more suited for a different type of regression (took a cursory look at logistic (about same is this one) and quadratic (worse than linear)). This simply means that we technically can't use the model to make accurate predictions. We're going to try to anyway, but just thought you should know***

The last model was very nice, a maximum of 9 variables each with a number attached, very clean, very easy to type out. Interactions muck things up. Makes it very unpleasant to look at and understand. This is the result I got at the end.

> summary(FinalModel)

Call:
lm(formula = AVERAGE ~ Age + HT + Wt + Pos + GP + G + A + PIM + 
    Corsi + Age:HT + Age:Wt + Age:Pos + Age:GP + Age:G + Age:A + 
    Age:PIM + Age:Corsi + HT:Wt + HT:Pos + HT:GP + HT:G + HT:A + 
    HT:PIM + HT:Corsi + Wt:Pos + Wt:GP + Wt:G + Wt:A + Wt:PIM + 
    Wt:Corsi + Pos:GP + Pos:G + Pos:A + Pos:PIM + Pos:Corsi + 
    GP:G + GP:A + GP:PIM + GP:Corsi + G:A + G:PIM + G:Corsi + 
    A:PIM + A:Corsi + PIM:Corsi + Age:HT:Wt + Age:HT:G + Age:HT:A + 
    Age:HT:Corsi + Age:Wt:Pos + Age:Wt:GP + Age:Wt:A + Age:Wt:PIM + 
    Age:Pos:GP + Age:Pos:Corsi + Age:GP:A + Age:GP:PIM + Age:G:A + 
    Age:G:Corsi + Age:A:PIM + Age:A:Corsi + HT:Wt:A + HT:Pos:G + 
    HT:G:PIM + HT:A:PIM + Wt:GP:PIM + Wt:GP:Corsi + Wt:G:Corsi + 
    Wt:A:PIM + Pos:GP:G + Pos:GP:PIM + Pos:GP:Corsi + Pos:G:A + 
    Pos:G:PIM + Pos:A:PIM + Pos:A:Corsi + GP:G:A + GP:A:PIM + 
    GP:A:Corsi + GP:PIM:Corsi + G:A:PIM + G:A:Corsi + G:PIM:Corsi + 
    HT:GP:G, data = GoDataNew[, -2])

Residuals:
    Min      1Q  Median      3Q     Max 
-141063  -19680    -909   15428  158995 

Coefficients:
                      Estimate      Std. Error     t value    Pr(>|t|)    
(Intercept)   -1.266e+07    5.855e+06    -2.163      0.031031 *  
Age                5.611e+05    2.230e+05     2.516      0.012170 *  
HT                 1.636e+05    7.895e+04     2.072      0.038792 *  
Wt                 6.010e+04    2.825e+04     2.127      0.033896 *  
Pos                 9.872e+05   5.258e+05     1.877      0.061041 .  
GP                 1.260e+04    1.622e+04    0.776       0.437905    
G                   -2.462e+05   1.765e+05    -1.395     0.163533    
A                   -2.195e+05   1.430e+05    -1.535     0.125386    
PIM              -3.244e+04   1.949e+04    -1.665     0.096601 .  
Corsi              8.987e+04   4.420e+04     2.033     0.042548 *  
Age:HT        -7.286e+03   3.005e+03     -2.425    0.015657 *  
Age:Wt        -2.655e+03    1.071e+03    -2.480    0.013475 *  
Age:Pos       -4.433e+04    1.800e+04    -2.462    0.014142 *  
Age:GP        -8.842e+02   5.432e+02    -1.628     0.104231    
Age:G            7.806e+03   5.692e+03     1.371     0.170864    
Age:A           -4.757e+03   4.267e+03    -1.115     0.265458    
Age:PIM        9.448e+02  5.468e+02      1.728    0.084611 .  
Age:Corsi     -3.625e+03  1.692e+03     -2.142    0.032653 *  
HT:Wt         -7.639e+02   3.770e+02     -2.026    0.043251 *  
HT:Pos          2.769e+03   3.740e+03      0.740    0.459394    
HT:GP           1.171e+02  1.251e+02       0.936   0.349746    
HT:G             3.265e+03  2.428e+03       1.345    0.179336    
HT:A             1.359e+03  2.033e+03       0.669    0.504082    
HT:PIM        4.540e+01  1.588e+02       0.286    0.775065    
HT:Corsi     -1.214e+03  6.079e+02      -1.997    0.046340 *  
Wt:Pos        - 6.374e+03  2.313e+03       -2.755   0.006076 ** 
Wt:GP         -1.198e+02  7.222e+01      -1.658    0.097884 .  
Wt:G             6.753e+01  6.763e+01       0.998    0.318557    
Wt:A             2.987e+03  5.676e+02       5.263    2.10e-07 ***
Wt:PIM        1.358e+02  7.027e+01       1.933    0.053806 .  
Wt:Corsi       1.322e+01  1.676e+01       0.788   0.430852    
Pos:GP          2.221e+03  1.397e+03       1.590   0.112499    
Pos:G            1.113e+05  5.420e+04       2.053   0.040605 *  
Pos:A           -5.476e+03  2.437e+03      -2.247   0.025080 *  
Pos:PIM       2.764e+03  7.851e+02        3.520   0.000470 ***
Pos:Corsi     -5.699e+03  3.016e+03      -1.890   0.059372 .  
GP:G           -1.979e+03  9.277e+02       -2.133   0.033383 *  
GP:A             5.974e+02  1.269e+02       4.709    3.23e-06 ***
GP:PIM        1.674e+02  9.042e+01        1.851   0.064708 .  
GP:Corsi      4.876e+02  1.542e+02        3.162   0.001660 ** 
G:A              -2.856e+02  4.594e+02      -0.622   0.534426    
G:PIM          4.904e+03  1.182e+03       4.150    3.90e-05 ***
G:Corsi        -3.395e+03  8.831e+02      -3.844    0.000136 ***
A:PIM          -4.578e+03  8.828e+02      -5.186    3.12e-07 ***
A:Corsi        -1.185e+03  3.165e+02      -3.745    0.000201 ***
PIM:Corsi     4.258e+01  2.841e+01       1.499   0.134557    
Age:HT:Wt   3.417e+01  1.427e+01   2.394 0.017037 *  
Age:HT:G      -1.145e+02  7.816e+01  -1.464 0.143729    
Age:HT:A       1.417e+02  6.057e+01   2.339 0.019725 *  
Age:HT:Corsi   4.739e+01  2.287e+01   2.072 0.038794 *  
Age:Wt:Pos     2.373e+02  8.654e+01   2.742 0.006325 ** 
Age:Wt:GP      5.046e+00  2.654e+00   1.902 0.057791 .  
Age:Wt:A      -2.195e+01  7.536e+00  -2.913 0.003741 ** 
Age:Wt:PIM    -4.628e+00  2.439e+00  -1.898 0.058295 .  
Age:Pos:GP    -1.271e+02  4.930e+01  -2.577 0.010240 *  
Age:Pos:Corsi  2.407e+02  1.178e+02   2.042 0.041620 *  
Age:GP:A      -2.300e+01  4.600e+00  -4.999 7.95e-07 ***
Age:GP:PIM     2.610e+00  1.891e+00   1.380 0.168259    
Age:G:A        4.859e+01  1.310e+01   3.709 0.000231 ***
Age:G:Corsi   -3.130e+01  1.741e+01  -1.798 0.072843 .  
Age:A:PIM     -9.063e+00  5.019e+00  -1.806 0.071549 .  
Age:A:Corsi    2.992e+01  1.070e+01   2.797 0.005355 ** 
HT:Wt:A       -3.423e+01  7.198e+00  -4.756 2.58e-06 ***
HT:Pos:G      -1.388e+03  7.291e+02  -1.904 0.057524 .  
HT:G:PIM      -5.810e+01  1.571e+01  -3.698 0.000241 ***
HT:A:PIM       4.971e+01  1.408e+01   3.531 0.000452 ***
Wt:GP:PIM     -1.064e+00  3.899e-01  -2.730 0.006561 ** 
Wt:GP:Corsi   -1.910e+00  7.849e-01  -2.434 0.015293 *  
Wt:G:Corsi     2.078e+01  4.032e+00   5.154 3.66e-07 ***
Wt:A:PIM       4.719e+00  1.667e+00   2.831 0.004823 ** 
Pos:GP:G       2.507e+02  1.269e+02   1.977 0.048633 *  
Pos:GP:PIM    -5.621e+01  1.589e+01  -3.539 0.000439 ***
Pos:GP:Corsi  -1.190e+02  2.720e+01  -4.376 1.47e-05 ***
Pos:G:A       -5.000e+02  2.686e+02  -1.862 0.063236 .  
Pos:G:PIM     -6.201e+02  1.405e+02  -4.413 1.25e-05 ***
Pos:A:PIM      2.685e+02  6.108e+01   4.396 1.34e-05 ***
Pos:A:Corsi    3.917e+02  1.181e+02   3.317 0.000974 ***
GP:G:A        -4.584e+00  2.955e+00  -1.551 0.121539    
GP:A:PIM       1.451e+00  1.076e+00   1.349 0.178011    
GP:A:Corsi     4.585e+00  1.974e+00   2.323 0.020563 *  
GP:PIM:Corsi  -2.023e+00  6.383e-01  -3.170 0.001619 ** 
G:A:PIM       -7.154e+00  2.910e+00  -2.459 0.014280 *  
G:A:Corsi     -2.414e+01  6.211e+00  -3.886 0.000115 ***
G:PIM:Corsi    1.143e+01  2.659e+00   4.298 2.07e-05 ***
HT:GP:G        2.472e+01  1.245e+01   1.986 0.047613 *  
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 44200 on 506 degrees of freedom
Multiple R-squared:  0.7897, Adjusted R-squared:  0.7547 
F-statistic: 22.61 on 84 and 506 DF,  p-value: < 2.2e-16

It's big, it's ugly, it's scary. And you can see the point where I gave up trying to make it into nice columns. Needless to say, I'm not going to type out the equation that this produces. The most important part of the summary is the Adjusted R-squared value of .7547. This says that 75.47% of the changes in average salary between players can be explained using the provided variables. It also means that 24.53% of the changes have a source unaccounted for, whether that's just stats that weren't included or immeasurables like "leadership" or "toughness" it's impossible to say.
Quick note: Corsi is actually significant this time. Not super significant, maybe, but still significant. It's hard to get a read as to how many dollars it actually contributes (or takes a way perhaps) but that's the problem with models with interaction.

Examples (random):

Jarred Tinordi (MTL): 22 years old, 78 inches tall (6'6"), 225 lbs, Defensemen, 13 Games Played, 0 Goals, 2 Assists, 19 Penalty Minutes, 0.4 Corsi.
Predicted salary = $983,967,   Actual salary = $850,500,  Difference = $133,467

Barret Jackman (NSH): 33 years old, 72 inches tall (6'), 203 lbs, Defensemen, 80 Games Played, 2 Goals, 13 Assists, 47 Penalty Minutes, 5.8 Corsi.
Predicted salary = $1,750,835,   Actual salary = $2,000,000,    Difference = $249,165

Jason Akeson (PHI): 23 yrs, 70 inches (5'10"), 190 lbs, Forward, 1 GP, 0 Goals, 1 Assits, 0 PIM 34.2 Corsi (WOW! Small sample size alert).
Predicted salary = $469,916,   Actual salary = $575,000,   Difference = $105,084
(*Worth noting: The NHL minimum salary is $550,000 per year)

Olli Jokinen (NSH): 35 yrs, 74 inches (6'2"), 210 lbs, Forward, 82 GP, 18 Goals, 25 Assists, 62 PIM, -1 Corsi.
Predicted salary = $3,787,174,  Actual salary = $2,500,000,  Difference = $1,287,174
(*first prediction off by over 1 million!)

Colby Robak (FLA): 23 yrs, 75 inches (6'3"), 194 lbs, Defensemen, 16 GP, 0 Goals, 1 Assist, 17 PIM, 1.98 Corsi.
Predicted salary = $764,333,  Actual salary = $675,000,   Difference = $89,333

So what's the conclusion? It missed on a couple and got close on a few. I think the difference for Akeson should actually be $25,000 since my model predicted  a minimum salary but I make the rules and I cut myself no slack (I'm tough on myself)
The Jokinen one is interesting. I think that my model overvalues age, and so Jokinen being 35 is a great thing as far as my model goes, not so great as far as real hockey goes.
As with all of these external factors come into play. Things like offseason surgery, bad/good locker room guy, legal issues don't get implemented into the statistical model but have a large impact on the real life money of these guys.

The refinement continues. My next task will be to use many more of the statistics I have available to me and/or break the skaters into forwards and defensemen.

I also think that the team a player comes from or goes to plays a role. A player from a team that just won the Cup is more valuable because they "know how to win" and a team that has been struggling and not in a nice market (*cough cough*Edmonton*cough cough*) will likely have to overpay free agents to convince them to join. I just don't know how to model that cleanly yet.

More to come

How much is a goal worth? Salary Projections I

Salary Projections I: Simple linear regression without interaction



Something that I've been thinking a lot about recently is NHL statistics, specifically, measuring the quality of a player quantitatively. This might be fundamentally impossible, after all, unmeasurable such as "leadership" or the ever-elusive "toughness" can't truly be measured and neither can true player skill. Toughness is often correlated with blocked shots, hits, and fights. There's also a more subconscious relationship between toughness and penalty minutes. I single out toughness (as opposed to leadership) because it's also correlated to negative things. Let's be honest, blocked shots are really only necessary when your team doesn't have the puck and are giving up shots that must be blocked (right Kris Russell?). The best we can do with toughness is use a combination of things to approximate the true value. Same for overall quality.

I believe that one of the best indicators of relative overall skill should be salary. Yes, there are players who are underpaid or overpaid relative to their peers, but we can all agree that players with higher skill also usually get paid more money. To try to develop a formula we all can use to figure out how much a player should be (or should have been) paid, I took the list of this past offseason's free agents and ran a simple linear regression. I kept it quite simple to start with, churning out a model using only a few stats. I used a mixture of counting stats and biographical stats (such as age) to a) keep it simple to understand the output b) try to hone in on a few common "buzzword" statistics and c) keep it simple to program since it's good to start and work your way up.

The statistics I used are Age, Height, Weight, Position (Forward vs Defense), Games Played the past year (a durability or toughness measure), Goals, Assists, Penalty Minutes, and stat-darling Corsi.
A couple of notes: There are not that many stats that we started with, and we'll pare down from there. You'll notice that position only has two values, forward and defense. As usual, this was a simplicity decision. When I build it up more, Forwards will be broken up into Centers and Wingers, as I think there's a difference there, but for now, Forwards and Defensemen wil do. Also Goalies are judged by a completely different set of statistics and so are not lumped in with the skaters.
These statistics were used to try to predict average salary. That is, the amount a player makes on average throughout the course of the contract, and (generally speaking) the amount of money that a team cannot use to acquire other players.



Model Without Interactions

Running the model without interactions is the best way to get easy-to-understand models. At the basic level, models without interaction show the impact specific statistics have on the response. For example: Player A and B are identical in every way, same height, weight, age, even their stats are identical EXCEPT for goals. If Player B has 1 more goal than Player B, how much more money is Player B expected to earn than Player A. A model without interactions show the value of a single stat more clearly.

Running model selection, the best model that we got, given these variables, is a model comprised of Age, Weight, Position, Games Played, Goals, Assists, and Penalties. This is interesting to me because of what didn't make the cut. Only two original stats didn't make the cut: Height and Corsi. Height is often used as a measure of size but I think the Weight value probably incorporates a bit of height in it (taller players are often heavier) and strength shows itself as a measurable more clearly in weight. Corsi is an interesting cut, considering the propensity of the statistics community to espouse it as a measure of player quality. There are a few potential reasons for this: 1) Corsi simply hasn't made its way into contract negotiations in a significant way. It's impact is overshadowed by the other measurables. Or 2) The model doesn't do a great job overall of measuring correlations, just the best job given the selected original variables (entirely possible as I'll show later).

**Something to note: The data was not very nice. What I mean by that is that the data must satisfy a number of qualifiers in order to be used to project forward and these qualifiers were not met. Since the purpose of doing all this is to have a projection, we're going to proceed. Sometimes the solution to this issue is more data points (we already have 600) and sometimes, the data just isn't "normal".**

Before proceeding further, I'd like to note that there were a few outliers in this data which I removed from the data set. They didn't seem to change the data too too much but I removed them for the sake of cleanliness. There were 12 of them and I'd take special note if there were something special to note about their data. Most of them were paid on the upper levels of salary ($4.5 million and up) but that's about it. After removing the outliers, the model selection process was run again and nothing changed. This was expected since the removed values were somewhat insignificant on the whole.

Skipping any more nitty-gritty stats stuff (the overall least interesting part of the whole situation), the results

We get a formula that looks like this:

AvgSalary = 23766*Age  + 3836*Weight  - 353809*Position - 9427*Games Played + 78071*Goals + 87140*Assists + 3024*Penalty Minutes - 522951

**Age is measured in years, Weight is measured in pounds, and Position is denoted as 1 for forwards and 0 for defensemen, Games Played is only for the previous year **

Some interesting notes:
- Age is viewed as a positive but games played is a negative. Age being positive makes sense because of the experience factor and the idea that team's know what they're getting with an older player. I don't know why games played is a negative, maybe a more complex model will show it.
- Assists are worth more than goals? Defensemen tend to make more money than forwards and they tend to get more assists than goals. This drives up the perceived value of assists versus goals. If we were to run this for just forwards (future idea?) then I would guess that goals would jump in value.
- Speaking of forwards versus defense, the "player" "loses" almost $354,000 per year just by being a forward. This makes sense in a couple of ways. First, there are less defensemen as forwards and so they are generally a little more valuable simply because there are less of them. Second, if you had a forward generate the exact same statistics as a defenseman (say, 4 goals, 19 assists or whatever), that forward would unlikely be as valuable as a defenseman who can produce at the exact same level  and, presumably, play defense.
-The y-intercept is impossible but that's not a huge deal since a player with zeros across the board is impossible too.

So now what? One of the things that we can do is determine how well the model actually performs. We don't need a training/testing set because this is regression and we are attempting to model the very data that created it.
So how did we do? Turns out, not that great. This model earned an adjusted R squared value of .6306. What this means in simple terms is that 63% of the changes in average salary from player to player is determined by the variables we selected (37% by some mystery variable(s) that was/were unaccounted for). It's not bad for a simple model but I would anticipate that some more complex stuff would model it a bit better.

Examples (kind of randomly selected):
Devante Smith-Pelly (ANA): 21 years old, 222 lbs, Forward, 19 GP, 2 G, 8 A, 2 PIM
      Projected average salary = $1,154,115.  Actual = $800,000.   Difference = $354,115

Curtis McKenzie (DAL): 23 years old, 205 lbs, Forward, 36 GP, 4 G, 1 A, 48 PIM.
      Projected average salary = $661,442. Actual = $675,000.   Difference = $13,558

David Steckel (ANA): 31 years old, 215 lbs, Forward, 34 GP, 1 G, 6 A, 4 PIM.
      Projected average salary = $977,215, Actual = $550,000.   Difference = $427,215

 Calvin de Haan (NJD): 22 years old, 187 lbs, Defenseman, 51 GP, 3 G, 13 A, 30 PIM.
      Projected average salary = $1,694,209.  Actual = $1,966,667.   Differences = $272,458

As is somewhat clear, the model works better for some players than others. McKenzie was predicted fairly accurately but Steckel's projected salary was 178% of what he actually earned.

So the model isn't fantastic, but it can do well enough to give a general ballpark.

The next step is to generate a model with interactions, the next level of complexity in this process, and see if the relationships between the variables can be used to more accurately project salary

Let's talk about Luck . Picks 4/16

Let's talk about luck. Specifically luck in a competition (specifically sports because of course). It seems to me that luck determines everything in competition involving two or more opposing entities. It's luck that potentially says that a defender reacts a certain way to an offensive move. A perfectly placed throw by a quarterback is not intercepted if the defender is more than a foot farther away from the ball than he should be. If the cornerback guesses an out route, and gets beat, that's unlucky. Or lucky if he guesses right. Skill raises the potential for lucky events. I can shut out LeBron James in 1-on-1 if I hit every single halfcourt shot I take (likely the only shot that won't get blocked) and he slips or dribbles off his foot every single time. Horrendously unlikely but still possible. His skill and my skill make that unlikely. Unless I get lucky.

Life is not really a video game, though. I wouldn't write a masters thesis on it.

The basketball regular season is over so this is a recap of my last day of picks. Here we go.

Results 4/14


Toronto (-1.5) over Boston. I continually pick the Raptors and I usually pay for it.
Celtics won by 2. Lost $100.
It makes sense that my last day of picks features a half point loss. Ugh. The Raptors have been a pain in my side for the last two months.

Indiana (-7.0) over Washington. I'm picking the desperate team in this one.
Pacers won by 4. Lost $100.
Pacers needed the win and got it, just not by as much as I needed them to.

LA Clippers (-10.0) over Phoenix. Neither team has anything to play for. I'll take the better one.
Clippers won by 11. Won $190.
I got a one point win to go with my half point loss. Nothing really to say.

Totals: Bets: $300.  Winnings: $190.  NET: -$110.  Record: 1-2
April totals:  NET: +$650.  Record: 25-16.

It was a good basketball season, not so much for the Wolves but the future is bright. That's what counts, right? (right Oilers fans?).

As far as my picks are concerned, I had two good months and two not so good months, though I believe February was the only one that I lost money in.

My NET for 2015 was $1250.  I made over $1000 in four months, not going to become a career gambler or anything. I never use real money on my bets and do not condone it but this was a fun exercise. Maybe I'll do it next season, who knows.

Good season.

Been a While. Picks 4/14

The reason I haven't written a blog in a while is because I got a job. As a result, my time has been more scarce. I have some free time today, so here's some picks. There aren't that many days left in the season. Today will be my last pick blog and then there will likely be a results blog at some point. The regular season is over tomorrow and I won't be picking for the playoffs. It's been a good run.

It's been a while, so expect even less analysis than usual.

Results 4/8

Boston (+1.0) over Detroit. Apparently Detroit is still in the playoff race. Go figure.
Celtics won by 10. Won $190.
 Not unexpected.

Chicago (-8.0) over Orlando. Odds: 1.86.  Don't like Orlando. Certainly like Chicago more. Do not particularly like this pick.
Magic won by 2. Lost $100.
Go figure.

Toronto (-3.0) over Charlotte. Am I a fool for picking Toronto again? They have to win again at some point?
Raptors won by 18. Won $190.
Woo.

Washington (-8.0) over Philadelphia. I feel like this a rarity these days, me not picking Philly when they're getting more than five points. And against Washington too, whom I love to pick against. I don't know. Guts!
Wizards won by 29. Won $190.
76ers playing to their skill level. Wizards playing to theirs

Atlanta vs Brooklyn. OFF THE BOARD. NO BET.  For some reason, Bet365 is not currently accepting bets for this game. Don't know why.


Indiana (-12.5) over New York. Paul George is back from a horrendous broken leg he suffered over the summer. Obviously he's not back to what he was last year but if he can bring the Pacers from about 65% of last year's skill to 75%, that should be more than enough to dominate the Knicks.
Pacers won by 16. Won $190.
Ugh. The Knicks

Cleveland vs Milwaukee. OFF THE BOARD. NO BET. Another one.

Memphis (-5.0) over New Orleans. Memphis is turning (have turned) a corner and are playing better. I think Gasol can mitigate Davis at least somewhat and everyone can take care of business. Kind of depends on which Tyreke Evans shows up.
Grizzlies by 36. Won $190.
Holy cow.

Houston (+6.0) over San Antonio. Classic Pop rest game potential.
Spurs won by 12. Lost $100.
Spurs.

LA Lakers vs Denver. OFF THE BOARD. NO BET


Sacramento vs Utah. OFF THE BOARD. NO BET


Dallas (-9.0) over Phoenix. Phoenix has really nothing to play for, Dallas does.
Mavericks won by 3. Lost $100.
I'm going to assume that Dallas was favored by 9 in this one.

Portland (-16.5) over Minnesota. Back to back? Traveling? Onuaku? Give me Portland.
Trailblazers won by 25. Won $190.
Ugh the Wolves.

Totals: Bets: $900.  Winnings: $1140.  NET: +$240.  Record: 6-3
April Totals: NET: +$760.  Record: 24-14.

Good day. Very good day. There aren't enough games today to drop me to the red for the month so that's nice.

The Games 4/14  Odds are 1.90 unless otherwise noted

Toronto (-1.5) over Boston. I continually pick the Raptors and I usually pay for it.

Indiana (-7.0) over Washington. I'm picking the desperate team in this one.

LA Clippers (-10.0) over Phoenix. Neither team has anything to play for. I'll take the better one.

Go Flames Go.

Flames, I guess? Picks 4/8

I really don't have anything to say today. Some days, a topic will jump out at me and be on my mind the entire time I make my picks. Other days, I have nothing. Today is one of the latter kinds. The only thing I have to say is that the Flames' magic number to get into the playoffs is 1. A Kings loss OR a Flames win gets them in. Each have two games to go, one of those is against each other. This is exciting and terrifying. I don't even know what I want anymore!

Yesterday's picks!!

Results 4/7

Charlotte (+6.0) over Miami. The Heat are injured and the Hornets are inconsistent. I'm taking inconsistent today
Heat won by 5. Won $190.
Close win for me and an unfortunate loss for Charlotte. Twitter indicates they had the game in their control and lost it. Their season is pretty much over at this point though I don't know if they're mathematically eliminated quite yet.

Atlanta (-7.5) over Phoenix. This is a trap since it's unknown how much resting the Hawks will do.
Hawks won by 27. Won $190.
Dominant win. I always feel slightly embarrassed when I pick a team that gets absolutely crushed. Like there should have been some indicator that that would happen. It's weird. Fortunately I guessed correctly yesterday.

Golden State (-5.0) over New Orleans. I think Golden State will attempt to snatch back some mojo before the playoffs start.
Pelicans won by 3. Lost $100.
Another loss for Golden State. I'm sure at this point they're coasting. Pelicans are a desperate team now and have now snatched the last playoff spot in the West.

San Antonio (-6.0) over Oklahoma City. I feel like the Spurs are just posturing for position and maybe even playing for playoff matchups. We'll see if that's true. They could easily rest.
Spurs won by 25. Won $190.
The Thunder lost their playoff spot and as Popovich said after the game "it wasn't a fair fight". The Thunder are very injured right now (as opposed to the Wolves who are "injured") and that has taken its toll. As good as Westbrook is, he can't defend everyone and I'm sure he's running out of gas a bit.

Minnesota (+10.0) over Sacramento. I actually think that the Kings is the correct bet but I typed Minnesota first so maybe it's destiny...? But seriously, the Kings should take advantage of this dumpster fire.
Kings won by 5. Won $190.
My intuition was correct! Destiny and all that. This is exactly what the Wolves want, good production from the rookies (Wiggins: 26 pts, LaVine: 21 pts) and a close loss. Fantastic. The Wolves actually played 9 players, including Arinze Onuaku (whom I have no idea who that is. Certainly didn't know he was on the Wolves)

LA Lakers (+17.0) over LA Clippers. Oh my goodness. 17? I'll take the Lakers, I guess? It's just a lot.
Clippers won by 5. Won $190.
Twitter jokes explained. The Lakers and the Clippers play in the same arena, just a different floor and the Clippers cover up the Lakers' banners. So when they play each other and people comment how the "Lakers are a much better team on the road", the joke is that they're actually still playing at home, just with a different floor and such. You're welcome. Anyway, the Clippers should have won by more.

Totals:  Bets: $600.  Winnings: $950.  NET: +$350.  Record: 5-1
April totals: NET: +$520.  Record: 18-11.

Great night. Getting into the homestretch of the picks season. There's only about two weeks left in the season so we're getting down to it.

Big night tonight. 13 games.

The Games 4/8  Odds are 1.90 unless otherwise noted

Boston (+1.0) over Detroit. Apparently Detroit is still in the playoff race. Go figure.

Chicago (-8.0) over Orlando. Odds: 1.86.  Don't like Orlando. Certainly like Chicago more. Do not particularly like this pick.

Toronto (-3.0) over Charlotte. Am I a fool for picking Toronto again? They have to win again at some point?

Washington (-8.0) over Philadelphia. I feel like this a rarity these days, me not picking Philly when they're getting more than five points. And against Washington too, whom I love to pick against. I don't know. Guts!

Atlanta vs Brooklyn. OFF THE BOARD. NO BET.  For some reason, Bet365 is not currently accepting bets for this game. Don't know why.

Indiana (-12.5) over New York. Paul George is back from a horrendous broken leg he suffered over the summer. Obviously he's not back to what he was last year but if he can bring the Pacers from about 65% of last year's skill to 75%, that should be more than enough to dominate the Knicks.

Cleveland vs Milwaukee. OFF THE BOARD. NO BET. Another one.

Memphis (-5.0) over New Orleans. Memphis is turning (have turned) a corner and are playing better. I think Gasol can mitigate Davis at least somewhat and everyone can take care of business. Kind of depends on which Tyreke Evans shows up.

Houston (+6.0) over San Antonio. Classic Pop rest game potential.

LA Lakers vs Denver. OFF THE BOARD. NO BET

Sacramento vs Utah. OFF THE BOARD. NO BET

Dallas (9.0) over Phoenix. Phoenix has really nothing to play for, Dallas does.

Portland (-16.5) over Minnesota. Back to back? Traveling? Onuaku? Give me Portland.

Lots of off the board games. I don't know what's going on. Maybe late in the season they don't worry as much about some games, or maybe there wasn't enough interest (I doubt that). Dunno. Turns out there's only 9 picks today.

Sports luck. Picks 4/7

I read an article by Samuel Arbesman on Wired.com where he interviewed Michael Mauboussin about his book The Success Equation: Untangling Skill and Luck in Business, Sports, and Investing. He presents a continuum of Luck vs Skill and their impact on the outcomes of various sports. *Please note that he uses a specific definition for Luck and that by saying a sport has more Luck involved, he does not say that skill is less important. He says that there are more random events that can swing the game in the favor of one team over another in some sports.*

On this continuum, basketball is the highest on the skill side and hockey is the lowest on the skill (highest luck, if you prefer), with soccer, baseball, and football in order of highest to lowest pure skill effect.

This is a wordier-than-it-needs-to-be example on why I pick NBA games. It is my belief, and Mr. Mauboussin agrees (though he uses actual measurements) that NBA games are "easier" to predict. Frequent readers will note that that does not seem to help me, but you can usually point to more concrete reasons in basketball. The number of possessions, the scoring system, the amount of players on the court all contribute to the better basketball teams usually winning the game.

As an example of this, let's briefly look at an impact play that happens in each sport besides baseball, a change of possession and quick score. *this is one example of basketball vs the others, not comparing the others against each other. There's a lot more that goes into all of this.*  In Football, this is a pick-6, punt/kickoff return touchdown, or fumble recovery and run. Soccer, Hockey, and Basketball, it's a steal or missed shot and counterattack/breakaway.

Football - Potentially responsible for between 1/5 to 1/3 of the scoring (give or take) for the entire game for the one team
Soccer - Generally responsible for between 1/3 and all of the scoring for the one team
Hockey - Could be between 1/4 to 1/2 for the one team
*These are all pure estimations off the top of my head of the average scoring amount in a typical game*
Basketball - Could be between 1/43 to 1/50 for the one team.

Each sport also experiences a momentum swing.

The point is that in basketball, the better team has many more opportunities to make up the one disastrous play. It's harder to pinpoint a "turning point" because of the sheer number of events that happen in each game. Basketball is an easier game to predict a winner, which is part of the reason why gambling requires spreads to create even bets.

I feel like it's easier to become good at predicting basketball games than most other sports.

*There are a lot of "well buts" that go along with only using one example to explain my point. There are holes and I'm sure I could think of counterarguments to supplement but I just don't feel like it*

Only one game last night, terrifying to me from a picks perspective

Results 4/6

Portland (+6.0) over Brooklyn. The Nets continue to bite me in the butt but I like Portland's chances in this game. This makes me think that Aldridge or Lillard isn't playing for some reason but six points is a lot.
Nets won by 10. Lost $100.
Aldridge didn't play. I thought that Portland would still win because I still don't think that Brooklyn is very good. Turns out I was wrong.

Totals: Bets: $100.  Winnings: $0.  NET: -$100.  Record: 0-1

April Totals: +$170.  Record: 13-10.

As I mentioned yesterday, I hate days like this where it can go great or terribly. It went terribly yesterday but, on the plus side, I could only lose a maximum of $100. And that's what I did.

Another small-ish night, with only 6 games.

The Games 4/7  Odds are 1.90 unless otherwise noted

Charlotte (+6.0) over Miami. The Heat are injured and the Hornets are inconsistent. I'm taking inconsistent today

Atlanta (-7.5) over Phoenix. This is a trap since it's unknown how much resting the Hawks will do.

Golden State (-5.0) over New Orleans. I think Golden State will attempt to snatch back some mojo before the playoffs start.

San Antonio (-6.0) over Oklahoma City. I feel like the Spurs are just posturing for position and maybe even playing for playoff matchups. We'll see if that's true. They could easily rest.

Minnesota (+10.0) over Sacramento. I actually think that the Kings is the correct bet but I typed Minnesota first so maybe it's destiny...? But seriously, the Kings should take advantage of this dumpster fire.

LA Lakers (+17.0) over LA Clippers. Oh my goodness. 17? I'll take the Lakers, I guess? It's just a lot.

Congrats to the Duke Blue Devils and their fans on the National Championship. It was a very good game even though I think Duke gained the benefit of some questionable foul calls to swing the momentum. I would have preferred a Wisconsin win but I like Coach K and Duke. Always have.