The Magnus Prediction Model, v3

Estimating Individual Impact on NHL 5v5 Shot Rates

April 20, 2020, Micah Blake McCurdy, @IneffectiveMath
(This model, "Magnus 3", is an updated version of last year's model, Magnus 2. In the interests of providing a self-contained documentation here, I've copied the explanation there with changes as appropriate.)

What's New

For old hands who want to know the quick rundown of what is new this season:


I would like to be able to isolate the individual impact of a given skater on shot rates from the impact of their teammates, their opponents, the scores at which they play, the zones in which they begin their shifts, the instructions of their head coach, their level of fatigue, how far away their bench is, and home-ice advantage. I have fit a regression model which provides such estimates. The most important feature of this model is that I use shot rate maps as the units of observation and thus also for the estimates themselves, permitting me to see not only what portion of a team's performance can be attributed to individual players but also detect patterns of ice usage.

Here, as throughout this article, "shot" means "unblocked shot", that is, a shot that is recorded by the NHL as either a goal, a save, or a miss (this latter category includes shots that hit the post or crossbar). I would prefer to include blocked shots also but cannot since the NHL does not record shot locations for blocked shots.

The most sophisticated element of Magnus is the method for estimating the marginal effect of a given player on shot rates; that is, that portion of what happens when they are on the ice that can be attributed to their individual play and not the context in which they are placed. We know that players are affected by their teammates, by their opponents, by the zones their coaches deploy them in, and by the prevailing score while they play. Thus, I try to isolate the impact of a given player on the shots which are taken and where they are taken from.

Although regression is more mathematically sophisticated than some other measures, it is in no way a "black box". As we shall see, every estimate can be broken down into its constituent pieces and scrutinized. If you are uneasy with the mathematical details but interested in the results, you should skip the "Method" section and just think of the method as like a souped-up "relative to team/teammate statistics", done properly.


I use a simple linear model of the form \( Y \sim WX\beta \) where \(X\) is the design matrix, \(Y\) is a vector of observations, \(W\) is a weighting matrix, and \(\beta\) is a vector of marginals, that is, the impacts associated to each thing, independent of each other things. Each passage of 5v5 play with no substitutions is encoded in the model as two rows, one row with the home team as the attacking team, where the response entry in \(Y\) is the pattern of shots taken by the home team, and another row with the road team as the attacking team, where the response entry in \(Y\) is their pattern of shots. I call such passages of play with no substitutions "stints", by analogy with the same term used in basketball research, although the presence of on-the-fly changes in hockey mean that some stints can be very short. Note also that a single stint can contain within it several stoppages of play, so long as the players on the ice do not change. A typical NHL season contains about a quarter million stints and so our design matrix \(X\) has about half a million rows. Happily, as we shall see momentarily, there are only about two thousand covariates, so \(X\) has about a billion entries, most of which are zero. This is computationally non-trivial but still tractable with a level head and some half-decent computers.


The columns of \(X\) correspond to all of the different features that I include in the model. There are broadly, two different types of columns. Some terms occur in pairs, one for offence, one for defence:

The remaining terms apply only to the offensive team:

Using such a small list of covariates ensures that there is no formal linear dependence among the columns (as there would have been had I included an overall intercept, or a column for tied score, for instance). Previous iterations of Magnus have skirted this line a number of times, which I am happy to avoid more cleanly now.


The entries in \(Y\), the "responses" of the regression, are functions which encode the rate at which unblocked shots are generated from various parts of the ice. An unblocked shots with NHL-recorded location of \((x,y)\) is encoded as a two-dimensional gaussian centred at that point with width (standard deviation) of ten feet; this arbitrary figure is chosen because it is large enough to dominate the measurement error typically observed by comparing video observations with NHL-recorded locations and also produces suitable smooth estimates.

One detailed example (with no-longer current players for either team, but no matter) should make the structure of the model clear:

Suppose that the Senators are the home team and the Jets are the away team, and at a given moment of open play two Senators players (say, Karlsson and Phaneuf) jump on the ice, replacing two other Senators. This begins a new "stint". The score is 2-1 for Ottawa, and the other players (say, Pageau, Hoffman, and Stone for Ottawa, against Laine, Ehlers, Wheeler, Byfuglien, and Enstrom) began their shift some time previously on a faceoff in Ottawa's zone. Play continues for 50 seconds, during which time Ottawa takes two unblocked shots from locations (0,80) and (-10,50) and Winnipeg takes no shots. This shift ends with the period and the players leave the ice. The game is the season-opener for Winnipeg, but Ottawa played their first game yesterday, using all of the named players except Phaneuf, who was scratched in that game for unspecified reasons, causing a great deal of unproductive twittering.

These fifty seconds are turned into two rows of \(X\) and two entries in \(Y\). First, the Ottawa players are considered the attackers, and the attacking columns for Pageau, Hoffman, Stone, Karlsson, and Phaneuf are all marked as "1". The Jets are considered the defenders and the defending columns for Laine, Ehlers, Wheeler, Byfuglien, and Enstrom are marked as "1". All of the other player columns, attacking or defending, are marked as "0". Because the Senators are winning 2-1, the score column for "leading by one" is marked with a "1" and the other score columns are marked as "0". Three of the Senators players are playing on-the-fly shifts, while the other two Senators skaters (that is, 40% of the skaters) are still playing the shift they began in their own zone, so the "home defensive-zone attacking" zone column is marked with "0.4". The zones that the Jets players began /their/ shifts in are not considered at this time. The Senators are the home team, so the "home intercept" column is marked with a 1. The column for "Guy Boucher, attacking coach" is marked with a one, while the column for "Paul Maurice, defending coach" is marked with a one, and all the other coaching columns are marked with zeros. Since all of Winnipeg players are well-rested, no rest columns are marked for defence. Phaneuf is well-rested for Ottawa, but the other four skaters played last night, so the column for "played last night, attackers", is marked with 0.8, since 80% of the attacking skaters did so. All the other other rest columns are marked with zeros. Corresponding to this row of \(X\), an entry of \(Y\) is constructed as follows: Two gaussians of ten-foot width and unit volume are placed at (0,80) and (-10,50) and the two gaussians are added to one another. This function is divided by fifty (since the stint is fifty seconds long); resulting in a continuous function that approximates the observed shot rates in the shift. Finally, I subtract the league average shot rate from this. This function, which associates to every point in the offensive half-rink a rate of shots produced in excess of league average from that location, is the "observation" I use in my model.

Second, the same fifty seconds are made into another observation where the Jets players are considered the attackers, and the Senators are the defenders. The attacking player columns for the Jets are set to 1, the defending columns for the Senators players are set to 1. Since the Jets are losing, the score column of "trailing by one" is set to one. Since all of the Jets skaters are in the middle of an offensive-zone shift, the "OZ" term is marked as 1; the Senators shift start locations are not considered here. The column for "Guy Boucher, defending coach" is marked with a one, while the column for "Paul Maurice, attacking coach" is marked with a one, and all the other coaching columns are marked with zeros. Since all of Winnipeg players are well-rested, no rest columns are marked for offence. Phaneuf is well-rested for Ottawa, but the other four skaters played last night, so the column for "played last night, defenders", is marked with 0.8, since 80% of the defending skaters did so. All the other other rest columns are marked with zeros. The two rows have no non-zero columns in common. Since the Jets didn't generate any shots, the associated function is the zero function; I subtract league average shot rate from this and the result is placed in the observation vector \(Y\).

The weighting matrix \(W\), which is diagonal, is filled with the length of the shift in question. Thus, the above two rows will each have a weight of fifty.

By controlling for score, zone, teammates, and opponents in this way, I obtain estimates of each players individual isolated impact on shot generation and shot suppression.


To fit a simple model such as \(Y = WX\beta \) using ordinary least squares fitting is to find the \(\beta\) which minimizes the total error $$ (Y - X\beta)^TW(Y - X\beta) $$ Since \(X\beta\) is the vector of model-predicted results (where \(\beta\) "is" the model), the difference between it and the observed results \(Y\) is a measure of error; forming the weighted product of \(Y-X\beta\) with itself is squaring this error (to ensure it's positive), and then we want to minimize this total error: hence the name "least squares".

When the entries of \(Y\) are numbers, this error expression is a one-by-one matrix which I naturally identify with the single number it contains, and I can find the \(\beta\) which minimizes it by the usual methods of matrix differentiation. To extend this framework to our situation, where the elements of \(Y\) are shot maps, I use a dissection of the half-rink into ten-thousand pieces, as a hundred-by-hundred grid. This divides the rink up into parcels one foot long by 0.85 feet wide, sufficiently coarse to permit efficient computation and sufficiently fine to appear smooth when results are gathered together. In particular, since the input shot data is smoothed into sums of gaussians before the regression is fit, we can compute the regression as if it were ten thousand separate regressions whose outputs are combined to form the maps for each term. It might be helpful to imagine a video broadcasting system, where input video is spliced into channels, each channel modified by appropriate filters for the display media at hand, and then each channel organized into an output which viewers can percieve as a single object. The practical benefit of this is that I can use the well-known formula for the \(\beta\) which minimizes this error, namely $$ \beta = (X^TX)^{-1}X^TWY $$ which makes it clear that the units of \(\beta\) are the same as those of \(Y\); that is, if I put shot rate maps in, I will get shot rate maps out.

However, I choose not to fit this model with ordinary least-squares, preferring instead to use generalized ridge regression; that is, instead of minimizing $$ (Y - X\beta)^TW(Y - X\beta) $$ as in ordinary least squares, I add three so-called ridge penalties, to instead minimize: $$ (Y - X\beta)^TW(Y - X\beta) \\ + (\beta-\beta_\Lambda)^T \Lambda (\beta - \beta_\Lambda) \\ + (\beta-\beta_K)^T K (\beta-\beta_K) \\ + (\beta-\beta_J)^T J (\beta-\beta_J) $$ Each ridge penalty has the same structure; the first term says that deviation of the model (that is, \(\beta\)) from the data is bad; the second term says that deviation of the model from the specified vector \(\beta_\Lambda\) is bad, and the matrix \(\Lambda\) controls how "bad" such deviation is to be considered. The three matrixes we use here (\(\Lambda\), \(K\), and \(J\), with their attendant constant vectors \(\beta_\Lambda\), \(\beta_K\), and \(\beta_J\)), are how we specify our prior beliefs about the things being modelled, after we know what it is we are doing but before we consider the data itself.

The historical penalty

Although the exposition here focusses on 2019-2020, the most-recent season of NHL hockey as I write this, in practice I fit this model successively, first on 2007-2008, the first season for which the NHL provides data at this level of detail, and then repeating the process for all subsequent seasons. Thus, after each season, I have a suite of estimates of player ability which I do not throw away. Since I am trying to estimate player ability (not performance), I take the opinion that our estimates ought to change slowly, since a player's athletic ability also usually changes slowly. Furthermore, the game of hockey itself also changes (that is, its rules change, and also teams in the aggregate draft and play differently) but does so slowly. Thus, every term in the model is biased towards its value from the previous season. This is done by taking \(\beta_\Lambda\) to be the \(\beta\) from the previous season, and populating the diagonal elements of \(\Lambda\) itself with the estimated precisions from the previous season. In 2007-2008, with no prior year of data to guide me, I use the zero vector instead.

The regularization penalties

The normal penalty

The next two penalty terms encode our prior knowledge about the NHL specifically, about the overall quality of the players and coaches in it. In addition to returning players individually, we know that players who play in the league are selected; they have been drafted or signed from other leagues; every one of them has a substantial body of work examined by their managers and coaches, in one way or another. Furthermore, the athletic abilities themselves which we are primarily interested in are constrained, very generally, by what we know about the possibilities of human performance, and ultimately by physics itself. In particular, this means that extreme estimates are unlikely for this reason, regardless of the happenstances of any on-ice observations. Thus, we impose a penalty on every skater towards "NHL average". This is the "usual" ridge penalty, \( (\beta-\beta_K)^T K (\beta-\beta_K)\), where \(\beta_K\) is conveniently the zero vector, since "league average" each season is the reference point we choose to use for our regression.

A truly disciplined modeller would have used the league average from the previous year, in order to maintain faithful observance of using only prior data in specifying the prior, but I have cheated slightly and used the data from the season at hand instead. I crave the reader's forgiveness.

The weird penalty

A "standard" ridge penalty, applied equally to all players of all abilities in the same direction (that is, towards zero), is consistent with an intuition that the distribution of talent in the league is roughly arranged as a normal distribution around its centre. However, as an "apex" league, which (mostly) gathers the best men's hockey players and (largely) does not relinquish them, we should expect the distribution of talent to be skewed towards better players. Specifically, we should expect the best players to be more better than the average players than the worst players are worse than average, since such low-performers can be replaced with plausibly-better players (even if these replacements have no NHL experience) but the high-performers have no reason to leave.

Therefore, as a crude way of making a prior which might result in such a right-skewed distribution of ability might result, I include a second, weaker penalty, not towards average but towards an ability slightly worse than average, both offensively and defensively. Specifically I choose a \(\beta_J\) to be the constant vector whose entries are -2% offensively, relative to league average, and +2% defensively, relative to league average. This is the "weird" ridge penalty, \( (\beta-\beta_J)^T J (\beta-\beta_J)\).

Although there are two penalties (one for J and one for K), you should think of them together as specifying, in a rough-and-ready sort of way, the kind of distribution of abilities we think is present in the league before try to estimate any particular individual's ability.

The relative strengths of the two penalties is encoded into the diagonal entries of \(K\) and \(J\) as follows:

Players that we expect before we see their on-ice results or circumstances to be of similar ability can be fused, that is, penalty terms can be introduced to encode our prior belief that they are similar. I have chosen to fuse the Sedins in this way, with a penalty term of weight 10,000, because they are twins. I don't consider any more-distant relation than twins as legitimate grounds for this kind of prior.

The model can be fit to any length of time; in this article I'll be describing the results of fitting it iteratively on each season from 2007-2008 through 2018-2019 in turn. For 2007-2008, we use "NHL average"; for later seasons I use the estimate from the previous season as the prior for each column, with NHL average for new players or coaches.

Computational Details

So, after all that, the thing we would like to put our impatient hands on is the vector \(\beta\) which minimizes the following expression: $$ (Y - X\beta)^TW(Y - X\beta) + (\beta-\beta_\Lambda)^T \Lambda (\beta - \beta_\Lambda) + (\beta-\beta_K)^T K (\beta-\beta_K) + (\beta-\beta_J)^T J (\beta-\beta_J) $$

Happily, the usual methods (that is, differentiating with respect to \(\beta\) to find the unique minimum of the error expression) gives a closed form for \(\beta\) as: $$ \beta = (X^TWX + \Lambda + K + J)^{-1}(X^TW Y + \Lambda \beta_\Lambda + K\beta_K + J\beta_J) $$ In effect, instead of assuming every season that the league is full of new players about whom we know nothing, we use all of the information from the last dozen years, implicitly weighted as seasons pass to prefer newer information without ever discarding old information entirely.

Persnickety folks, who might wonder if there really is a unique minimum to the complicated error expression we wish to minimize, may rest assured that it suffices for \(\Lambda\), \(K\), and \(J\) to all be positive semi-definite, which they are. (For the etymologically and historically curious, ridge regression was invented in the first place by folks who were interested in solving problems where the matrix \(X^TWX\) was not invertible because a certain subspace of the columns of \(X\) was "too flat" in some quasi-technical sense. The artificial inserted matrix \(\Lambda\) adds a "ridge" in this notional geometry and makes the matrix conveniently invertible. The Bayesian "prior" interpretation, so important to my approach here, was discovered somewhat later.)



Every column in the regression corresponds to a map of shot rates over a half-rink. The simplest two terms are the home-ice term and the "Long change" term for second period play.

All of the maps are depicted here are to be understood as relative to league average expected goals for the season in question. Regions in red show more-and-more-dangerous shots coming from a given region of the ice than average, and blue regions show fewer-and-less-dangerous shot patterns than average. White regions see shot patterns that are roughly as dangerous as average. A full explanation of this expected goals model can be found here and the cleverness required to encode xG rates in pictures here.

For convenience, the xG rate of the term, relative to baseline 5v5 xG rate, is also shown in the neutral zone. So, displayed above, the home ice advantage in xG rates is +4.2%, which means that simply being the home team (in addition to any benefit gained by matchups) is associated with generating a pattern of shots likely to result in 4.2% more goals per hour than league average, given average shooting and goaltending talent.


The six score terms are as follows:

As we know, trailing teams dominate play, even though they still usually lose. Perhaps surprisingly, the effect of trailing by one goal, by two goals, or by three or more goals appears to be largely similar. On the other hand, leading teams generate fewer shots, with the effect becoming more and more pronounced as the lead grows. Unusual as this asymmetry may seem, it is consistent with recent research of mine showing that leading teams mostly drive score effects on shot rates.


The eight fatigue terms are as follows:

These terms are additive, so a given player might have played last night and also three days ago, so the impact of fatigue on that player can be obtained by adding both of those maps. Predictably, the effects of fatigue are negative, and the strongest effect is from having played the previous night, but having played two days ago is also non-trivial. The "played four days ago" terms seem very unimportant and in the future I might well delete them.


The three zone terms are as follows:

The on-the-fly zone impacts are blank by definition, since we considered on-the-fly starts as the reference. As expected, starting in the offensive zone helps boost shot rates considerably, and starting in your own zone depresses your shot rates even more. Perhaps surprisingly, starting a shift in the neutral zone depresses shot rates nearly as much; the blue lines (or, more to the point, the offside rules) are formidable obstacles.

Seasoned veterans of my work will remember what a dog's breakfast the zone treatment was in past models. I was keenly alive to the possibility that, since shift start locations for one team do not necessarily balance between the two teams—for instance, defensive-zone starts for one team may not necessarily correspond to offensive-zone starts for the other team, some or all of whom may instead by continuing shifts that were started elsewhere. However, in practice most shifts are balanced in this way, and including defensive terms separately caused not-quite-linear-dependence relationships to appear in my design matrix columns. This was not technically insurmountable but gave me the heebie-jeebies and inflated the condition number of the matrixes that I needed to invert, so I've taken the chance to simplify the treatment this season.

There is no need to worry that players who start many shifts in their own zone won't get the "credit" they deserve for this reason; any responsibility for the shots they allow in this circumstance will have to be shared among their own defensive ability, that of their teammates, the offensive ability of their opponents, and, crucially, the "offensive zone start" term which demand its share of the blame in the bulk of the shifts our intrepid d-zone hero starts in their own zone, if not all.


One obvious reason to construct such a model is to suss out the abilities of players, which is intrinsically interesting. However, we can also make comparisons about how much each of the various factors in our models affect play.

"Raw" on-ice results

Before I describe the model outputs, I turn first to the raw observed results from the 2019-2020 regular season.

This graph is constructed as follows: for every skater who played any 5v5 minutes, compute the xG created and allowed by their team while they were on the ice; this produces a point \((x,y)\). Then, form the density map of all such points, where each point is weighted by the corresponding number of minutes played by the player in question. For ease of interpretation, I've scaled the xG values by league average, so that a value of \((5,5)\) on the graph means "threating to score 5% more than league average, and also threatening to be scored on 5% more than league average". As is my entrenched habit, the defensive axis is inverted, so results that are favourable to the skater in question appear in the top right (marked "GOOD" so that there can be no doubt). The contours are chosen so that ten percent of the point mass is in the darkest region, another ten percent in the next region, and so on. The final ten percent of the point mass is in the white region surrounding the shaded part of the graph. For convenience the weighted sum of the values (the centre of mass of the distribution, if it were a real life mountain) is marked with a red dot.

Player Marginals

Repeating the above process with the individual player marginal estimates gives the following graph in green. The overall shape is still broadly normal, with the centre-of-mass between \((0,0)\) and \((-2,2)\) like you would expect given the biases we introduced towards those points. One guiding principle of mine is that I don't include any terms in the model itself that identify players by position, since I would like to be able to measure differences between positions.

With that in mind, here is the same density as above, but only for forwards:


and again for defenders:


Not surprisingly, forwards generate somewhat more offence, on average, but, perhaps more surprisingly (to some), forwards are also marginally better at suppressing offence. The difference between the centre-of-masses of the two distributions is a little less than two per cent of league average xG per hour. This difference in estimated ability is somewhat smaller than in previous years, and the reason for this change reveals an interesting technical subtlety about the ridge penalty terms. The first version of magnus (published in the summer of 2018) had a single ridge penalty, towards zero. The second version, published in the summer of 2019, also had a single penalty, but not towards zero, instead towards the previous season's estimate. I did not realize until recently that the two penalties are conceptually different forms for prior information—one about distribution and the other about individuals.

Including the individual information without the distributional information allows a sort of "slow-motion overfitting", where players who have unusually good or unusually bad years create for themselves unreasonable expectations not just for them but also for their teammates; if you play well with a good player who was even better last season than they are now, a model with individual priors but not league-wide priors will always prefer to give the credit to the previously-extremely-good player instead of to you. A similar effect will tend to "lock in" blame to players who are weak but who were previously weaker still. For reasons that are not entirely clear to me, the unusually good single-year performances in recent NHL seasons have nearly all come from forwards, and the unusually bad ones from defenders. These extreme values accumulated in sufficiently large numbers to skew Magnus 2 estimates for forwards quite substantially. The solution, as the reader will have already deduced, is to include distributional penalties as well as individual penalties, both in a position-agnostic manner, so that this overfitting is mitigated and we can still observe the differences between the abilities of the players who play different positions.

Teammate Impact

Once I have individual estimates for players in hand, I can make many interesting secondary computations to show the distribution of various effects. Most obviously, for a given player, I can form the sum of the player estimates of all the given player's teammates, weighted by their shared icetime, and then multiply it by four (since every player has four teammates at 5v5). These estimates of teammate quality can then be graphed as above:

Here we see a definite skew towards good players; that is, most players are playing with better than average players. This fits our intuition, since better players play mostly with one another and also play considerably more minutes than weaker players when coaches are making rational decisions, which is most of the time.

Opponent Impact

The same computation can be done for any given players 5v5 opponents: form the sum of all of their isolates, weighted by head-to-head icetime, and then multiply by five, since every player has five opponents at 5v5. This is graphed below in red:

First, notice that the scale on the axes is smaller than for teammates; more discussion of that will follow later. The variation in competition quality is more pronounced offensively, with the range of defensive ability faced by players is much smaller.

The fact that both the teammates and competition distributions are skewed off-zero only makes sense if they are correlated, as we know intuitively that they are. If we compute the net "skater impact" on each player, that is, if we add together the effect of each player's teammates and the effect of their competition, we obtain a distribution that is roughly normal around zero, as expected.

Score Impact

The distribution of scores is sharply linear and almost entirely team driven. Players on weak teams play more while trailing, and players on strong teams play more when leading, with smaller variations within individual teams according to coaching choices. The skew towards "FUN" is consistent with the reference state being taken as tied, the state which many coaches are content to play conservatively within, especially in third periods.

Zone Impact

The net effect of zone starts for shifts is smallish; bigger than score but slightly smaller than competition. The skew towards "DULL" is consistent with the reference state being on-the-fly starts, which begin in open play and permit shots for both teams much sooner than a standing start like faceoff shifts.

Coach Impact

Since coaches change and players change teams, not every player is affected by head coaching the same amount. That said, there are only around thirty-five or forty head coaches in the league each year, so the distribution of coaching effects on players is lumpier. (The blob near the GOOD/DULL border is players who played most of their minutes under Ralph Krueger, who has presided over considerable improvement in the Sabres defence).


Strictly speaking, residuals for a regression refer to the differences between the individual observations (that is, each stint) and the predicted shot rates for that stint. As a perhaps more insightful alternative, for each player, I can compute the difference between their raw (observed) 5v5 on-ice results and what I would expect from computing the model outputs associated with the observed players and the zones and scores and home-ice advantage they player under. This is shown below:

This makes a sort of "goodness of fit" measure.

Relative Scale

In the above, I have shown each distribution on its own scale, so that I could discuss the shape of each one in turn. However, to understand their relative importance, they should all be placed on the same scale, which I have done below (except the residuals):

Here there are many interesting insights to be had:

Previous Work and Acknowledgements

Using zero-biased (also known as "regularized") regression in sports has a long history; the first application to hockey that I know of is the work of Brian MacDonald in 2012. His paper notes many earlier applications in basketball, for those who are curious about history; also I am very grateful for many useful conversations with Brian during the preparation of the first version of Magnus. Shortly after MacDonald's article followed a somewhat more ambitious effort from Michael Schuckers and James Curro, using a similar approach. Persons interested in the older history of such models will be delighted to read the extensive references in both of those papers.

More recently, regularized regression has been the foundation of WAR stats from both Emmanuel Perry and Josh and Luke Younggren, who publish their results at Corsica and Evolving-Hockey, respectively.

Finally, I am very thankful to Luke Peristy for many helpful discussions, and to the generalized ridge regression lecture notes of Wessel N. van Wieringen which were both of immense value to me.

As far as I can tell, the extension of ridge regression to functions (that is, shot maps instead of just single numbers) is new, at least in this context.

Player and Coach Results

While my research has always been free to the public and will remain so, I restrict access to the specific regression outputs for players and coaches to website subscribers. To gain access, please subscribe at the $5 tier (or higher, of course); then after logging in you should be able to find coaching terms, and individual player terms are on the career pages for each player.