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"# Normalized Pick Rate\n",
"## (A Modeling Diary)\n",
"\n",
"\n",
"Normalized Pick Rate, or **NPR**, is a new metric I've developed to reflect the inferred pick orders of top players from the daily card data available on 17Lands, specifically ALSA, # Seen, and # Picked. NPR estimates the pick rate of the card at pick one of a random pack (at any point in the draft), and is measured on a logarithmic scale. The normalization is such that a value of 0.0 represents even odds to be chosen (i.e. 13:1 against), and each increment represents a doubling of those odds, so that a value of 1.0 gives 13:2 odds and a 10.0 gives 13:1024 odds, or about a 99% probability of being chosen from a given full pack. A -8.0, typical of a basic land, represents 3328:1 odds of being chosen, effectively nil. That doesn't mean that basic lands were actually chosen first every 3329 packs, it's just a consequence of the statistical modeling which doesn't fully capture edge-case behavior. Here's an example from MH3. Fair warning, this is going to be one where I share lots of code."
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\n",
"\n",
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\n",
"\n",
"\n",
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\n",
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Normalized Pick Rate
\n",
"
\n",
"
\n",
"
Name
\n",
"
NPR
\n",
"
\n",
"\n",
"\n",
"
\n",
"
Guide of Souls
\n",
"
4.98
\n",
"
\n",
"
\n",
"
Writhing Chrysalis
\n",
"
3.23
\n",
"
\n",
"
\n",
"
Eldrazi Repurposer
\n",
"
1.38
\n",
"
\n",
"
\n",
"
Twisted Riddlekeeper
\n",
"
0.28
\n",
"
\n",
"
\n",
"
Hydra Trainer
\n",
"
−2.11
\n",
"
\n",
"
\n",
"
Swamp
\n",
"
−7.17
\n",
"
\n",
"\n",
"\n",
"\n",
"
\n",
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\n",
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"┌──────────────────────┬──────────┐\n",
"│ Name ┆ NPR │\n",
"│ --- ┆ --- │\n",
"│ str ┆ f64 │\n",
"╞══════════════════════╪══════════╡\n",
"│ Guide of Souls ┆ 4.981666 │\n",
"│ Writhing Chrysalis ┆ 3.227463 │\n",
"│ Eldrazi Repurposer ┆ 1.378317 │\n",
"│ Twisted Riddlekeeper ┆ 0.277058 │\n",
"│ Hydra Trainer ┆ -2.11209 │\n",
"│ Swamp ┆ -7.16626 │\n",
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"source": [
"import polars as pl\n",
"from great_tables import GT\n",
"\n",
"from spells import summon # my package for aggregating 17Lands public data\n",
"from deq import ext # the official definition of NPR is in here\n",
"\n",
"df = summon(\n",
" \"MH3\",\n",
" [\"npr\"],\n",
" filter_spec={'$and': [{'player_cohort': 'Top'}, {'lhs': 'format_day', 'op': '>', 'rhs': 7}]},\n",
" extensions=ext\n",
").filter(pl.col('name').is_in([\n",
" 'Guide of Souls', \n",
" 'Writhing Chrysalis', \n",
" 'Eldrazi Repurposer', \n",
" 'Twisted Riddlekeeper', \n",
" 'Hydra Trainer', \n",
" 'Swamp'\n",
"])).sort('npr', descending=True)\n",
"\n",
"GT(df.rename({'name': 'Name', 'npr': 'NPR'})).fmt_number('NPR').tab_header('Normalized Pick Rate')"
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"My hope is that whenever you are struck wondering how top players are prioritizing different cards early, you will drop by [DEq](https://magic-flea.com/on-draft/deq.html) to check the NPR stat. I still recommend using the DEq metric itself to make early picks, although this approach is viable as well (as we'll see) and you might benefit from using it to break near ties.\n",
"\n",
"One important caveat: since the available metrics on the card data page are from all three packs, this metric represents an average prioritization across the three packs. P1P1 behavior looks somewhere different for bombs that require color commitment. You'll find that colorless cards generally have the higher NPR ratings, while in fact top players tend to pick committal bombs at a higher rate early. This metric won't be able to infer that prioritization.\n",
"\n",
"I don't have much else to say of interest from a Magic point of view, but I'd like to document the model. Since I can do whatever I want, I thought I would take this opportunity to walk you all through the process of creating of a new metric, including the details of how I put my code together. Hopefully it can provide some inspiration for those of you training to enter a quantitative field, or a new point of view for other professionals. Ultimately we will get into advanced-undergraduate territory, but I hope there's something to learn for those at earlier stages of their journey. If all you're interested in is getting some useful stats to pick Magic cards, this is your cue to jump back in the queue.\n",
"\n",
"## Inspiration\n",
"\n",
"If you haven't seen Dan Brooks's [Statistical Drafting](https://statisticaldrafting.com/) I hope you'll check it out, it's an amazing resource, a fantastic application of a neural net architecture to Magic drafting, and a wonderful interactive quantitative presentation. While I was excited to see the work in general, I was also excited because I have been waiting for someone to produce competing quantitative pick orders for me to test against DEq using my [substitution analysis](p1-strategy). These are totally independent pieces of analysis, so there's no reason the results would be biased in favor of DEq and there's every reason to hope that the best metric may \"win\". I'll recompile the data now to make sure I have his latest numbers and run it again. I'll show the broad expected win rate change as well as the entropy graph, which I'll explain. I'll use the abbreviation \"SD\" to refer to the Statistical Drafting model below."
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import os\n",
"from deq.p1_strategy import all_metrics_analysis\n",
"from deq.plot import p1_delta_bar, p1_line_plot\n",
"\n",
"sets = [\"EOE\", \"FIN\", \"TDM\", \"DFT\"]\n",
"metrics = [\"danb\", \"deq\", \"pick_equity\", \"gih_wr_17l\"] # pick equity is a stand-in for ATA where higher is better\n",
"metric_context = pl.concat([\n",
" pl.read_csv(\n",
" os.path.expanduser(f\"~/imports/danb-{set_code.lower()}.csv\")\n",
" ).with_columns(pl.lit(set_code).alias('expansion'))\n",
" for set_code in sets\n",
"])\n",
"# equivalent to the data used by SD\n",
"metric_filter = {\n",
" '$and': [\n",
" {'player_cohort': 'Top'}, {'lhs': 'format_day', 'op': '>', 'rhs': 2}\n",
" ]\n",
"}\n",
"results_filter = {'lhs': 'format_day', 'op': '>', 'rhs': 3} \n",
"labels = {'danb': 'SD'}\n",
"\n",
"result = all_metrics_analysis(\n",
" sets=sets, \n",
" metrics=metrics, \n",
" results_filter=results_filter,\n",
" metric_filter=metric_filter,\n",
" metric_context=metric_context,\n",
")\n",
"\n",
"p1_delta_bar(result, metrics=metrics, metric_labels=labels)\n",
"p1_line_plot(result, mode=\"entropy_loss\", metrics=metrics, metric_labels=labels)"
]
},
{
"cell_type": "markdown",
"id": "12ea3555-85aa-4d96-ba92-80c81ce53a6e",
"metadata": {},
"source": [
"I find this to be a beautiful result that shows how analysis can uncover hidden structure. Without knowing anything about Dan's methodology, we can start to put together a picture of what SD is doing and how effective it is. First, let's look at the win-rate results. According to the chart, on average, players would gain about 0.28% of game win rate in PremierDraft by following SD P1P1 rankings instead of what they would normally do, then drafting as usual from that point. Now, SD is a complete draft bot, not just a P1P1 ranking, but this is all I'm able to analyze by looking at results after the fact. While the SD rankings don't quite compete with DEq, they do lead to stronger results on average than GIH WR. Given that GIH WR is the community standard for card quality, and that most people would be better off using it more, I think it points to a fundamental efficacy of the model. \n",
"\n",
"The second chart is what really got me thinking. The chart depicts the information loss using the implied P1P1 rankings instead of the observed distribution. As an example, suppose that a given ranking always prioritized commons P1P1. That means that all of the draft data from players who chose mythics, rares, and uncommons would be unusable, and we would need to infer win rates from a much smaller sample. That loss of information is what the \"Representative Entropy Loss\" graph captures, separated out by the player skill estimate, since entropy is a measurement of information. We immediately see that SD is doing something special here. It is capturing the preferences of Strong and Elite drafters better than ATA, even though ATA is itself measuring the preferences of top drafters (all of the statistics here use an in-sample approximation of the \"top\" player cohort found on 17Lands). My immediate conclusions from the chart is that the training of SD is specifically trying to capture the preferences of top drafters, which I confirmed with Dan.\n",
"\n",
"The point I wasn't prepared for was the viability of such a strategy. I had been using ATA as a shorthand for player preference in my analysis, and since following the metric is not particularly viable compared to even GIH WR, I hadn't done much follow-up analysis. But now we are seeing that a metric trained on the actual preferences of top drafters is viable, and those preferences are significantly different from what ATA expresses. Why is that?\n",
"\n",
"Well, I knew the answer. Any one who listens to Sierkowitz knows that ATA is not really measuring preferences, and given it's distorted view of rankings, you are often better off using ALSA as an impression of the overall availability of cards. Now I was aware of this, and use ATA in DEq because it measures what I need to measure — the opportunity cost paid by the actual drafters whose win rates we measure. Using it as a pick order proxy was simply convenience. I had the ranking at my fingertips, and showing the contrast of GP WR and ATA to the metric combination, DEq, demonstrated the power of combining them.\n",
"\n",
"As the \"extract new information from daily card data\" guy, my mind immediately got spinning on whether we could infer this kind of priority from the available data on 17Lands. Let's remember why ATA doesn't actually measure preferences. For cards that are highly prioritized by average players, but not by top players, those cards disappear from packs before they are able to be chosen, so the only time they are chosen are early. This implies that, for a given rarity, the cards players actually want will end up in their pile more often than the ones that simply disappear. My first idea was to look at those pick counts and normalize by rarity, and combine that information with ATA statistically.\n",
"\n",
"But this is where the real skill, maybe the only skill, of quantitative problem solving comes in. You get a feeling, from experience, that your solution is not the best solution, and, if the best solution is not apparent, you spend a few days turning it over in your mind. Something about using rarity felt wrong and wouldn't work anyway, given the uncertain structure of modern packs. Surely there is a way to extract that information in a cleaner way from the available data. Now, the answer is probably obvious since it's in the title, or because you listen to Sierko even closer than I do, but for whatever reason it took me a few days. The question just sort of sat in my head, and I would turn it over in the odd hours. That's how you solve a problem. Pick rate, of course. Cards taken divided by cards seen. Both counts are available on 17Lands, even for the smallest sample sizes, and that should be enough to differentiate players' preference for cards with the same ATA. But how do you differentiate cards with different ATA and different pick rates? We need a model.\n",
"\n",
"The next step is to extract and analyze that pick rate data, and model its relationship to ATA understand what we are trying to extract. The basic idea is this: for a given card, with a given fixed preference by the cohort, for each later pack that card appears in, the higher its pick rate will be, since the quality it's competing against declines. Therefore a card with lower ATA but equivalent preference to another card should have a lower overall pick rate because it was not available in those later packs for its pick rate to increase. I start to conceive of ways to observe that trend, but first I'd like to know I'm on the right track. Let's take a look at what kind of card quality information is encoded in the pick rate itself, using our tried-and-true approach above."
]
},
{
"cell_type": "code",
"execution_count": 7,
"id": "e1fa0eb9-d74b-41f2-9fb1-0367106204de",
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"from spells.columns import agg_col\n",
"\n",
"ext = {\n",
" 'pick_rate': agg_col(pl.col('num_taken') / pl.col('num_seen'))\n",
"}\n",
"labels = {**labels, 'pick_rate': 'Pick Rate'}\n",
"metrics = ['danb', 'pick_rate', 'pick_equity']\n",
"\n",
"result = all_metrics_analysis(\n",
" sets=sets,\n",
" metrics=metrics, \n",
" results_filter=results_filter,\n",
" metric_filter=metric_filter,\n",
" metric_context=metric_context,\n",
" extra_ext=ext\n",
")\n",
"\n",
"p1_delta_bar(result, metrics=metrics, metric_labels=labels)\n",
"p1_line_plot(result, mode=\"entropy_loss\", metrics=metrics, metric_labels=labels)"
]
},
{
"cell_type": "markdown",
"id": "4b827f4d-96c0-49a4-942b-cef6722abd95",
"metadata": {},
"source": [
"Another wonderful result! Pick rate is perfect, it's exactly the metric we need to represent the missing information about preferences. It's more viable than ATA as a method for picking cards, and the shape of the entropy curve perfectly matches the one for SD. We have every reason to hope that some additional modeling of the relationship between pick rate and ATA will get us very close to true preference rankings. Now we need an idea for how to do that, but fortunately I can build off an analysis I've been turning to for a long time — the pick-by-pick charts. Normally I graph how as-picked win rate changes with pick location, but in this case, for a given card, I can graph pick rate against pick number and I should see that theoretical increase. Before we do that, it will be better to understand a better model for pick rate than pure probabilities. Probabilities are not practical for modeling preferences directly. For one thing, they are bounded below and above by zero and one, respectively, and bounds can be inconvenient to deal with in statistical estimation. Furthermore, there are important differences that we need to be able to distinguish near a boundary. An A- rare might be chosen out of most packs, but when it happens to appear opposite an A+, we want to clearly distinguish those cases. We might even hope for some representation that can model preferences in a stable way across packs.\n",
"\n",
"## Urns and Logits\n",
"Let's construct a tractable model for preference. Suppose we have a card $c$ and we want to assign it a value that represents its chance of being taken out of any particular pack — a weight $w_c$. Then for a pack with cards $c_j$ for $j = 1,...,k$, with some $c_j = c$ and weights $w_{c_j}$, we say the probability $c$ is chosen is \n",
"\n",
"$$\n",
"p_c = \\frac{w_c}{\\sum_j^k w_j}.\n",
"$$\n",
"\n",
"This is an assumption, a simple model analogous to a uniform distribution that allows us to represent varying levels of preference. It reflects an urn model with $w_c$ copies of card $c$ in any selection round against any other set of cards, although in our case $w_c$ need not be a whole number. If $w_c$ is very large relative to the other choices, $p_c$ is close to 1, and if it is very small (close to zero), $p_c$ is close to zero, assuming the other weights are larger. When $c$ is the only card in the pack, we have simply $p_c = w_c/w_c = 1$ regardless of $w_c$, which is good because that's how draft works as well.\n",
"\n",
"\n",
"This simple model gives us the power to express the full scale of card preference from trash commons to mythic bombs. The model asserts that preference is a one-dimensional determinant of choice, which is contrary to how a real drafter might consider things like wheel potential and signals sent, but it will do for our purposes. Our next task is to consider how to estimate $w_c$.\n",
"\n",
"An important step is to separate the effects of the other variables on $w$. We apply a logarithm to move into arithmetic space:\n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\log(p_c) &= \\log\\left(\\frac{w_c}{\\sum_j^k w_j}\\right)\\\\\n",
"&= \\log(w_c) - \\log\\left(\\sum_j^k w_j\\right).\n",
"\\end{align}\n",
"$$\n",
"\n",
"This quantity $\\log(w_c)$ is so important that we work with it directly, letting $\\theta_c = \\log(w_c)$, so that the probability expression, or \"log likelihood\" for one pack, becomes \n",
"\n",
"$$\n",
"l_c = \\theta_c - \\log\\left(\\sum_j^k e^{\\theta_j}\\right).\n",
"$$\n",
"\n",
"The term likelihood is used rather than probability when considering different models for the same event rather than vice versa. This is called a \"softmax\" model for those of you who may know the models but not the mathematical motivation behind them, and all of the derivations below are for variations on softmax, in case you'd like to search up additional reading. Suppose the cards besides $c$ have collective fixed weight $A$, and solve\n",
"\n",
"$$\n",
"p = \\frac{e^{\\theta}}{A + e^{\\theta}},\n",
"$$\n",
"for $\\theta$, getting \n",
"\n",
"$$\n",
"\\theta = \\log\\left(\\frac{Ap}{1-p}\\right) = \\log\\left(\\frac{p}{1-p}\\right) + \\log A.\n",
"$$\n",
"The first expression is the standard sigmoid function, and the ratio $p/(1-p)$ is known as the \"odds\", as it exactly corresponds to the quantity used in odds-making. If you haven't seen this before, it's a great way to motivate the standard sigmoid function.\n",
" \n",
"When comparing cards to each other with equivalent context, the added quantity $\\log A$ is effectively arbitrary (as we'll see), so the quantity $\\theta$ is often known as the \"log odds\", and in our situation the relative odds of two alternatives are determined by comparing their weights: the odds of card $c_a$ heads up vs $c_b$ can be expressed as $e^{\\theta_a} : e^{\\theta_b}.$ Since odds are compared as ratios, log odds are compared as differences. In other words, a card with log odds one greater than another is a factor of $e$ more likely to be chosen from any given pack. We use the natural logarithm for calculations, but in the final metric it will be a simple matter to rescale to use base two, giving the interpretive scheme at the top of this article.\n",
"\n",
"Since the shift is arbitrary, we will choose a baseline such that $\\theta = 0$ at even odds to be chosen P1P1. Assumptions aside, this is good enough motivation to begin looking at the relationship between pick rate and pick number. Pick rate will directly estimate $p$, and the logit transformation gives us a way to get a handle on the full range of values (\"logits\" is a synonym for \"log-odds\" that is the standard way to express the scale). We will calculate the \"pick rate logits\" and plot against pick number for each card. We'll also restrict to pack one to focus on the inference of P1P1 value, and pick out some cards with a range of pick orders.\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"id": "a9cd05c5-61b1-4155-99ee-2e7d2aaa04d0",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"from deq.plot import line_plot\n",
"\n",
"ext = {\n",
" **ext,\n",
" 'pick_rate': agg_col(pl.col('num_taken') / pl.col('pack_card')),\n",
" 'pick_rate_logits': agg_col((pl.col('pick_rate') / (1 - pl.col('pick_rate'))).log() - pl.lit(1.0 / 13.0).log())\n",
"}\n",
"\n",
"pack_one_filter = {'$and': [metric_filter, {'pack_num': 1}]}\n",
"\n",
"names = [ \n",
" 'Ambling Stormshell',\n",
" 'Karakyk Guardian',\n",
" 'Roiling Dragonstorm',\n",
" 'Humbling Elder', \n",
" 'Caustic Exhale',\n",
" 'War Effort',\n",
" 'Fortress Kin-Guard',\n",
" 'Temur Devotee',\n",
" 'Boulderborn Dragon', \n",
" 'Wingspan Stride'\n",
"] \n",
"\n",
"df = summon(\n",
" \"TDM\",\n",
" columns=[\"pick_rate_logits\", \"num_taken\"],\n",
" group_by=[\"name\", \"pick_num\"],\n",
" filter_spec=pack_one_filter,\n",
" extensions=ext\n",
").filter(\n",
" pl.col('num_taken') > 20\n",
").pivot(\n",
" on=\"name\", index=\"pick_num\", values=\"pick_rate_logits\"\n",
").sort(\"pick_num\").select(pl.col('pick_num').alias('Pick Num'), *names)\n",
"\n",
"ax = line_plot(df, title=\"Pick Rate Logit vs Pick Num\", y_label=\"Pick Rate Logit\", return_ax=True)\n",
"ax.axvline(x=1, color=\"grey\", linestyle=\"--\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"id": "7e3e12d1-b7e1-4b5e-a1e1-aa06137da39a",
"metadata": {},
"source": [
"Our hopes for this chart were well-founded. The logit transformation nicely spaces out the pick rates, and we see the expected increase in slope as pick number increases. In fact, aside from War Effort, the shape of the plots almost seem to imply a perfect gradient field, which would be ideal for inferring preferences. How would those underlying preferences manifest in the plot? Remember that under the assumption that the remaining cards in the pack have total preference independent of the given card, we can use the pick rate logit as the estimator. That assumption will be most nearly true in P1P1, so we'd roughly like to trace the lines out to the intercept at pick one and read off the ratings. By the way, we expected an increase, but was it reasonable to expect a linear relationship? Just a bit of additional modeling can tease out the implied assumption there. Suppose that the logit function has the following form:\n",
"\n",
"$$\n",
"\\log\\left(\\frac{p_j}{1-p_j}\\right) = \\theta + cj,\n",
"$$\n",
"\n",
"then reversing our previous derivations shows that \n",
"\n",
"$$\n",
"\\begin{align}\n",
"\\frac{p_j}{1-p_j} &= e^\\theta e^{cj} \\\\\n",
"p_j(1 + e^\\theta e^{cj}) &= e^\\theta e^{cj} \\\\\n",
"p_j &= \\frac{e^\\theta}{e^{-cj} + e^\\theta} \\\\\n",
"\\end{align}\n",
"$$\n",
"So linear growth in the pick rate by pick would represent an exponential decay in the quality of the remaining cards. That corresponds to the case where the chosen card represents roughly a fixed fraction of the overall power remaining in the pack, which is a reasonable starting point. Certainly we could calculate the slope of best fit with a weighted average of the slopes in the charts for the individual cards, interpolate from the $(\\text{ATA}, \\text{Pick Rate Logit})$ point to $x=1$, and call it a day.\n",
"\n",
"That said, if we want to be as precise as possible in our inference, there are a few difficulties that present themselves. First of all, we should be using ALSA, not ATA. ALSA has denser data and corresponds to the mean of the denominator of the pick rate, which should be more stable. Now for the issues — for one thing, rarity continues to throw a wrench in things. For commons, the remaining cards have a higher power level than is the case for rares, since there is an extra rare in the pack. Additionally, the shape isn't quite linear, especially right at $x=1$, there seems to be a kink downward and there is a steeper slope for later-pick cards. But the biggest problem is something you might have noticed in the code — the \"Num Seen\" stat from 17Lands does not double-count cards that wheel, which is what you need to do to properly measure the pick rate. A card that wheeled and then was taken was only taken half the time it was seen, but the \"seen\" pick rate according to 17Lands is 100%. We can still use the metric, but the effect on later-picked cards will be skewed. I decided I wasn't satisfied with things as they stood, and it was time to bring out bigger guns.\n",
"\n",
"## Maximum Likelihood Estimation\n",
"\n",
"Rather than try to guess at a vague intercept, let's directly estimate the preference parameter $\\theta_c$ for each card, and then find an appropriate model in our two parameters ALSA and Pick Rate. As a side effect we will implement something sophisticated, mathematically beautiful, and potentially \n",
"useful. I won't give a blow-by-blow of the derivation and development of the estimator for $\\theta$. It's something I'd like to do down the road, as there are many interesting applications to limited Magic, but we are in too deep as it is. Let me briefly give you the model, because I think there are some of you out there like me who have wrestled with this simply on a notational level and might find this useful. Suppose we have packs $i = 1, ..., n$, all cards enumerated $j = 1, ..., m$, then let $\\delta_i, j = 1$ if card $j$ appeared in pack $i$ and $0$ otherwise, and let $\\epsilon_{i, j} = 1$ if card $j$ was chosen in pack $i$ and $0$ otherwise. Then the likelihood of all observations is the product over all observed packs is \n",
"\n",
"$$\n",
"L(\\theta) = \\prod_{i}^n \\frac{\\sum_j^m \\epsilon_{i, j} e^{\\theta_j}}{\\sum_j^m \\delta_{i, j} e^{\\theta_j}}.\n",
"$$\n",
"\n",
"This is a useful form for users of the 17Lands public data because the variables correspond exactly the data available. We will streamline things notationally with vector representations of $\\mathbf{\\epsilon_i} = (\\epsilon_{i, 1}, ..., \\epsilon_{i, m})$, with $\\mathbf{\\delta_i}$ and $\\mathbf{\\theta}$ defined as $m$-dimensional vectors similarly. Take the log and the log likelihood $l$ is given by \n",
"\n",
"$$\n",
"l = \\sum_i^n \\mathbf{\\epsilon_i} ^T \\mathbf{\\theta} - \\log\\left( \\mathbf{\\delta_i}^T e^\\mathbf{\\theta} \\right),\n",
"$$\n",
"\n",
"where the $^T$ symbol represents matrix multiplication by a transpose column vector, i.e. the dot product. We get to pull the sum through the log in the left term because OM1 doesn't exist and you take one card per pack in Magic. This is the quantity we want to maximize, and it should be nicely convex, so the plan is to take two derivatives and use Newton's method. The solution will be known as the maximum likelihood estimation, or MLE. I'll give those forumulas for the real nerds out there and skip the implementation (for now). First, the gradient,\n",
"\n",
"$$\n",
"\\nabla l = \\sum_i^n \\mathbf{\\epsilon_i} - \\frac{\\mathbf{\\delta_i} e^\\mathbf{\\theta}}{Z},\n",
"$$\n",
"\n",
"where $Z$ is the partition function (total pack weight) $\\delta_i ^T e^{\\mathbf{\\theta}}$, and the elementwise multiplication $\\mathbf{\\delta}_i e^{\\mathbf{\\theta}}$ is the weight vector, which means $ \\mathbf{p_i} = \\mathbf{\\delta_i} e^\\mathbf{\\theta}Z^{-1}$ is the vector of probabilities of each card being chosen in pack $i$. That gives the elegant form\n",
"\n",
"\n",
"$$\n",
"\\nabla l = \\sum_i^n \\mathbf{\\epsilon_i} - \\mathbf{p_i}.\n",
"$$\n",
"\n",
"The gradient is the vector direction we would like to move to increase the likelihood expressed by the model, using the analogy of climbing a mountain by pointing your feet in the steepest direction. I wanted to get to this point because it's formulas like this that illustrate the power and clarity that mathematical analysis can bring. Let me explain. The expression is the sum of vector differences $\\mathbf{\\epsilon} - \\mathbf{p}$, one for each pack. Suppose that there is only one pack in the sample. Then the gradient is zero when $\\epsilon = \\mathbf{p}$, that is, when the probability of choosing the chosen card is 1 and all other probabilities are zero. In general, for the card chosen in a given pack $c$, the gradient contribution is $1-p_c$, which is positive, meaning it contributes towards increasing the value of $\\theta_c$ proportional to the expectation that it wouldn't be chosen, and for all other cards in the pack, the gradient contribution is $-p_c$, meaning they contribute negatively proportional to the expectation that they would have been chosen. Thus in one tiny expression, we see encoded all of the necessary information to optimize $\\mathbf{\\theta}$.\n",
"\n",
"Simple formulas like this are very often at the heart of what we call \"the algorithm.\" Rather than solve large optimization problems, services will record choices with simple vectors like this and use them to update a global or personalized parameter set in a totally distributed way. The parameters often will represent the likelihood that you're expected to click on a given widget, exactly analogous to a draft. As an exercise, develop a model to order a feed of YouTube thumbnails! The scheme is so simple that you might think of it without any analysis at all, but the fact that it relates back to a macro likelihood model means we can bring additional analysis to bear to optimize the training rate, calculate loss functions, and ensure stability.\n",
"\n",
"In our case, we are looking for a batch optimization, and to apply Newton's method we will need the Hessian matrix, the second derivative of the loss function, which is\n",
"\n",
"$$\n",
"H(l(\\mathbf{\\theta})) = - \\sum_i^n \\text{Cov}(\\mathbf{p_i(\\mathbf{\\theta)}}).\n",
"$$\n",
"Another beautiful result which you can verify by index-chasing with the chain rule. The nice thing about this one is the covariance matrix is inherently positive semi-definite, and positive definite if we restrict to well-supported selections, so we can rely on a stable, unique solution.\n",
"\n",
"Let's skip to applying the optimal weights, which are trained using pack-one data after day seven for top players.\n",
"\n",
"## Modeling NPR\n",
"\n",
"We're looking for a relatively simple model of pick rate and ALSA to fit the estimated parameters $\\theta_c$ across multiple sets. We withhold FIN and EOE as test sets, and train on the play booster sets MKM, OTJ, MH3, DSK, BLB, FDN, DFT, and TDM. We would like to capture the model with the fewest terms possible based on ALSA and \"Pick Rate Seen\", the pick rate based on the \"Num Seen\" stat from 17Lands, using the logit transformation calling it \"PRL\" for \"Pick Rate Logit\". From our chart, it appears a linear model will be close but not perfect, so we will test quadratic models with various numbers of terms. I'm going to ahead and derive the linear model for you because we did it for the softmax and that's what I always do when I want to make sure I've got it right. The general model is if there are $m$ cards with target value $\\theta$ and a $m \\times k$ matrix of statistics $X = (X_1, ..., X_k)$, we want to find $k$ parameters $A = (a_1, ..., a_k)$ such that the square error\n",
"\n",
"$$\n",
"E = (XA - \\theta)^T(XA - \\theta)\n",
"$$\n",
"\n",
"is minimized. Again we look at the gradient with respect to $A$, and you can take the time index chasing to see that it is analagous to the basic calculus with the expression\n",
"\n",
"$$\n",
"\\nabla E = 2X^T(XA - \\theta).\n",
"$$\n",
"\n",
"Set equal to zero and solve, and under the assumption that $X^TX$ is invertible, we get\n",
"\n",
"$$\n",
"A = (X^TX)^{-1}X^T\\theta.\n",
"$$\n",
"\n",
"Then we can test the closeness of fit by calculating the variance of the residual $\\mathbf{Var}(XA - \\theta)$ and comparing to the variance of $\\theta$ on the withheld test set, which is equivalent to checking the magnitude of the error term $E$ above.\n",
"\n",
"Of course there are standard solvers for these things. What are the statistics $X$? We get to pick those. The vector of ones is a good place to start to fit the intercept. We'll take vectors of ALSA and PRL, as mentioned, and call them $\\mathbf{s}$ and $\\mathbf{t}$. Then for a quadratic model, we want some terms like $\\mathbf{s}^2$, $\\mathbf{st}$, or $\\mathbf{t}^2$ consisting of element-wise products. I'll pick a model by comparing the residuals on the test set. Ready to write some code."
]
},
{
"cell_type": "code",
"execution_count": 9,
"id": "493b59c9-bb3a-4b86-8288-206d62907dfd",
"metadata": {},
"outputs": [],
"source": [
"all_sets = [\"MKM\", \"OTJ\", \"MH3\", \"BLB\", \"DSK\", \"FDN\", \"DFT\", \"TDM\", \"FIN\", \"EOE\"]\n",
"test_sets = [\"FIN\", \"EOE\"]\n",
"\n",
"metric_filter = {\n",
" '$and': [\n",
" {'player_cohort': 'Top'}, {'lhs': 'format_day', 'op': '>', 'rhs': 7}\n",
" ]\n",
"}\n",
"\n",
"ext = {\n",
" **ext,\n",
" 'pick_rate_logit_seen': agg_col((pl.col('num_taken') / (pl.col('num_seen') - pl.col('num_taken'))).log() - pl.lit(1.0 / 13.0).log())\n",
"}\n",
"\n",
"stat_df = summon(\n",
" all_sets,\n",
" [ \"pick_rate_logit_seen\", \"alsa\" ],\n",
" group_by = [\"expansion\", \"name\"],\n",
" filter_spec = metric_filter,\n",
" extensions = ext\n",
").filter(\n",
" pl.col('pick_rate_logit_seen').is_finite() & pl.col('alsa').is_finite()\n",
")"
]
},
{
"cell_type": "markdown",
"id": "7f52543a-15cb-4f98-91fd-591682b733b7",
"metadata": {},
"source": [
"Above we set up the filters and test sets. We use a 7-day cutoff here because that's what I picked when I set up the maximum-likelihood estimation code. The dataframe collects the finite values of ALSA and Pick Rate Logit by name and expansion. Next, calculate the MLE-optimal preference parameters and extract the vectors of training data."
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "bb3b6cdb-4b98-4cb6-962f-3d1a1ae8698a",
"metadata": {},
"outputs": [],
"source": [
"from deq.mle import pick_priority\n",
"\n",
"theta_concat = []\n",
"for set_code in all_sets:\n",
" theta_concat.append(pick_priority(set_code, pack_one_only=True).with_columns(pl.lit(set_code).alias('expansion')))\n",
"\n",
"theta_df = pl.concat(theta_concat)\n",
"\n",
"join_df = stat_df.join(theta_df, on=[\"expansion\", \"name\"])\n",
"\n",
"train = join_df.filter(~pl.col('expansion').is_in(test_sets))\n",
"\n",
"alsa = train['alsa'].to_numpy()\n",
"prl = train['pick_rate_logit_seen'].to_numpy()\n",
"theta = train['theta'].to_numpy()"
]
},
{
"cell_type": "markdown",
"id": "52a7861e-6342-4335-8657-b2ef6a952afd",
"metadata": {},
"source": [
"Next we'll check out a few choices of model, including the linear model and a few variations of quadratic models."
]
},
{
"cell_type": "code",
"execution_count": 11,
"id": "3465ae0b-fdb1-45c3-ae83-e92a7e2e6edd",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"\n",
"def get_model_params(alsa, prl):\n",
" ones = np.ones(alsa.shape)\n",
" return {\n",
" 'alsa': np.stack([ones, alsa], axis=1),\n",
" 'prl': np.stack([ones, prl], axis=1),\n",
" 'multilinear': np.stack([ones, alsa, prl], axis=1),\n",
" 's_quad': np.stack([ones, prl, alsa, alsa ** 2], axis=1),\n",
" 't_quad': np.stack([ones, alsa, prl, prl ** 2], axis=1),\n",
" 'st': np.stack([ones, alsa, prl, alsa * prl], axis=1),\n",
" 'full_quad': np.stack([ones, alsa, prl, alsa ** 2, prl ** 2, alsa * prl], axis=1),\n",
" }\n",
"\n",
"model_params = get_model_params(alsa, prl)\n",
"solutions = {model: np.linalg.lstsq(params, theta)[0] for model, params in model_params.items()}"
]
},
{
"cell_type": "markdown",
"id": "ef70bd5a-215e-4842-b461-a27b707f51eb",
"metadata": {},
"source": [
"Finally we return to the test data and calculate $R^2$, the number representing how well the model captures the variance of the parameter. The higher the better, and we expect a number in the 90s if our picture from before reflects the regularity of the general behavior."
]
},
{
"cell_type": "code",
"execution_count": 12,
"id": "ead76e30-db60-41f6-97a2-00dfaa73d9d7",
"metadata": {},
"outputs": [
{
"data": {
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"
"
]
},
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"output_type": "display_data"
}
],
"source": [
"ext = {\n",
" **ext,\n",
" 'npr': agg_col(2 * pl.col('pick_rate_logit_seen') - 0.16 * pl.col('alsa') - 0.04 * pl.col('alsa').pow(2))\n",
"}\n",
"labels = {**labels, 'npr': 'NPR', 'theta': 'MLE Theta'}\n",
"metrics = ['danb', 'pick_rate', 'npr', 'theta']\n",
"metric_context = metric_context.join(theta_df, on=['name', 'expansion'])\n",
"\n",
"result = all_metrics_analysis(\n",
" sets=sets,\n",
" metrics=metrics, \n",
" results_filter=results_filter,\n",
" metric_filter=metric_filter,\n",
" metric_context=metric_context,\n",
" extra_ext=ext\n",
")\n",
"\n",
"\n",
"p1_delta_bar(result, metrics=metrics, metric_labels=labels)\n",
"p1_line_plot(result, mode=\"entropy_loss\", metrics=metrics, metric_labels=labels)"
]
},
{
"cell_type": "markdown",
"id": "4b84b12e-fe66-4d7e-8033-067faecf08cd",
"metadata": {},
"source": [
"We could have hoped for better performance compared to Pick Rate, but it is certainly the case the the model better captures preferences than the pick rate data alone, so hopefully the community will find it useful. The MLE solution also did a great job of capturing that top player preference, the highest I've seen from any metric yet. That's all for now, drop by in a few weeks for a deep dive into data drafting the new powered cube!\n",
"\n",
"## Postscript: Revisiting Bad Decisions\n",
"\n",
"So, I went ahead and published the above along with the new metric. Then cube came out, and I noticed Black Lotus had a NPR value of 26. That's not really supposed to happen. According to the rubric given, an NPR of 26 should correspond to an odds of being picked of about one to six million. It turns out indeed that every player in the sample did choose Black Lotus, but that extreme value isn't supported by the data. The problem extended to the moxes, which had NPR values above 10, or 99% or so, but were not chosen at that rate in the sample.\n",
"\n",
"The core issue was my attempt to infer pack one pick orders using data from all three packs. For cards participating in one of Magic's five colors, the pick rate drops off after pack one naturally, but that isn't the case for many colorless bombs. Therefore, attempting to infer the pack one pick rate would require making that differentiation. Instead, I think it's more natural to accept the natural limitations of the data, and give the inferred pick rate applicable across the three packs. That's the data we have, and you shouldn't try to reach beyond the scope of your data without a good model and good evidence. So let's fix this up. I've updated the `pick_priority` model to use all three packs, so the only change will be inferring the three-pack priority using the ALSA and pick rate data instead of just the pack one priority. Let's do it now.\n"
]
},
{
"cell_type": "code",
"execution_count": 16,
"id": "e0b84a2b-074b-4135-84c4-3105bae29fc2",
"metadata": {},
"outputs": [
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Model
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$R^2$
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alsa
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84.74%
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prl
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"GT(_tbl_data=shape: (7, 2)\n",
"┌─────────────┬──────────┐\n",
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"│ str ┆ f64 │\n",
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],
"source": [
"theta_concat = []\n",
"for set_code in all_sets:\n",
" theta_concat.append(pick_priority(set_code).with_columns(pl.lit(set_code).alias('expansion')))\n",
"\n",
"theta_df = pl.concat(theta_concat)\n",
"\n",
"join_df = stat_df.join(theta_df, on=[\"expansion\", \"name\"])\n",
"\n",
"train = join_df.filter(~pl.col('expansion').is_in(test_sets))\n",
"\n",
"alsa = train['alsa'].to_numpy()\n",
"prl = train['pick_rate_logit_seen'].to_numpy()\n",
"theta = train['theta'].to_numpy()\n",
"\n",
"model_params = get_model_params(alsa, prl)\n",
"solutions = {model: np.linalg.lstsq(params, theta)[0] for model, params in model_params.items()}\n",
"\n",
"test = join_df.filter(pl.col('expansion').is_in(test_sets))\n",
"\n",
"alsa = test['alsa'].to_numpy()\n",
"prl = test['pick_rate_logit_seen'].to_numpy()\n",
"theta = test['theta'].to_numpy()\n",
"\n",
"test_params = get_model_params(alsa, prl)\n",
"\n",
"var = np.var(theta)\n",
"r_2_df = pl.DataFrame([{\n",
" 'Model': model, \n",
" '$R^2$': (var - np.var(X @ solutions[model] - theta))/ var\n",
"} for model, X in test_params.items()])\n",
"\n",
"GT(r_2_df).fmt_percent('$R^2$')"
]
},
{
"cell_type": "markdown",
"id": "1a32b18a-f865-4a61-b1c7-8986fb9cf8b8",
"metadata": {},
"source": [
"The fit on the model quadratic in ALSA is even better here, but a new source of annoyance arises. Here is the model implied by the model parameters:\n",
"\n",
"$$\n",
"\\text{NPR} = -1.55 + 1.3 \\cdot t + 0.36 \\cdot s - 0.08 \\cdot s^2.\n",
"$$\n",
"\n",
"There's a problem here. The whole idea of the model is that the normalization corrects for the increase in pick rate as ALSA increases. In order to do so, it is necessary that the model is decreasing in ALSA, but our new model is increasing until ALSA 4.5 when the quadratatic term takes over. What's the deal? Well, a statistical fit doesn't care about our model hypothesis, it's just optimizing the fit in the data. It's relatively rare that pick rate stays contant while ALSA increases, so it's not statistically necessary for the model to fit the observed behavior. If we want a model that represents the simple behavior we predicted, then we have to restrict the model to those variables. Let's do that instead."
]
},
{
"cell_type": "code",
"execution_count": 18,
"id": "7054c08f-e511-4539-ab7a-7cd1c2e5e76a",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([ 0.0796952 , -0.11987252, -0.04171515])"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"solution = np.linalg.lstsq(np.stack([np.ones(alsa.shape), alsa, alsa * alsa], axis=1), theta - prl)\n",
"solution[0]"
]
},
{
"cell_type": "code",
"execution_count": 62,
"id": "e94da57e-46e8-4fd2-b1ea-a963f7a10f00",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"np.float64(0.9662250787793721)"
]
},
"execution_count": 62,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X = np.stack([np.ones(alsa.shape), alsa, alsa * alsa], axis=1)\n",
"1 - np.var(X @ Out[59][0] - (theta - prl))/var"
]
},
{
"cell_type": "markdown",
"id": "d079ea2a-816b-4295-a3aa-3c7341f7a9bb",
"metadata": {},
"source": [
"The new model is a lot more elegant, and predicts the three-pack prioritization metric better than the old one ($R^2$ of 96.6%). Here's the model written out:\n",
"\n",
"$$\n",
"NPR = t-0.12 \\cdot s - 0.04 \\cdot s^2.\n",
"$$\n",
"\n",
"That's the NPR model used in the MH3 values way at the top of this needlessly long blog post, converted to base 2. We'll wrap things up with a refreshed fit to our pick curves and a final look at the substitution analysis. We have to adapt all of our plots to capture three packs of data."
]
},
{
"cell_type": "code",
"execution_count": 24,
"id": "2fd0508d-a78e-40f0-8be9-d66ebd8b4454",
"metadata": {},
"outputs": [
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"def model_curve(x, s, t):\n",
" return t - 0.12 * (s - x) / ( s - 1 ) * s - 0.04 * ((s - x) / ( s - 1 ) * s) ** 2\n",
" \n",
"sample_df = stat_df.filter(pl.col('name').is_in(names) & (pl.col('expansion') == 'TDM'))\n",
"\n",
"df = summon(\n",
" \"TDM\",\n",
" columns=[\"pick_rate_logits\", \"num_taken\"],\n",
" group_by=[\"name\", \"pick_num\"],\n",
" filter_spec=metric_filter,\n",
" extensions=ext\n",
").filter(\n",
" pl.col('num_taken') > 20\n",
").pivot(\n",
" on=\"name\", index=\"pick_num\", values=\"pick_rate_logits\"\n",
").sort(\"pick_num\").select(pl.col('pick_num').alias('Pick Num'), *names)\n",
"\n",
"ax = line_plot(df, title=\"Pick Rate Logit vs Pick Num\", y_label=\"Pick Rate Logit\", return_ax=True)\n",
"ax.axvline(x=1, color=\"grey\", linestyle=\"--\")\n",
"for name in names:\n",
" row = sample_df.filter(pl.col('name') == name)[0]\n",
" s = row['alsa'][0]\n",
" t = row['pick_rate_logit_seen'][0]\n",
" ax.scatter(s, t)\n",
" x = np.arange(1, s, 0.01)\n",
"\n",
" ax.plot(x, model_curve(x, s, t), linestyle=\"--\")\n",
"\n",
"plt.show()\n",
"\n",
"ext = {\n",
" **ext,\n",
" 'npr': agg_col(pl.col('pick_rate_logit_seen') - 0.12 * pl.col('alsa') - 0.04 * pl.col('alsa').pow(2))\n",
"}\n",
"labels = {**labels, 'npr': 'NPR', 'theta': 'MLE Theta'}\n",
"metrics = ['danb', 'pick_rate_logit_seen', 'npr', 'theta']\n",
"metric_context = metric_context.join(theta_df, on=['name', 'expansion'])\n",
"\n",
"result = all_metrics_analysis(\n",
" sets=sets,\n",
" metrics=metrics, \n",
" results_filter=results_filter,\n",
" metric_filter=metric_filter,\n",
" metric_context=metric_context,\n",
" extra_ext=ext\n",
")\n",
"\n",
"\n",
"p1_delta_bar(result, metrics=metrics, metric_labels=labels)\n",
"p1_line_plot(result, mode=\"entropy_loss\", metrics=metrics, metric_labels=labels)"
]
},
{
"cell_type": "markdown",
"id": "42320552-c322-4378-9387-2acfbc452c40",
"metadata": {},
"source": [
"A bit funny that after all that, the simple pick rate does as well or better at making first picks. In any case, I think the metric does its job. It's reasonably simple, an adjustment to taken over seen using ALSA, and does give the best available representation of the preferences of top drafters. Certainly this has gone on long enough. Let me know if you have any suggestions of questions, and I hope you find the new metric valuable."
]
},
{
"cell_type": "code",
"execution_count": null,
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