‘Happy’ Election Eve. Here Are My Politifact & POTUS Endorsement Dashboards

Tomorrow is election day in the United States, and like many I’ve been obsessed with politics during the last few months. I channeled this into the creation of some political dashboards and I’d like to share my 2 favorites with you. You’ve probably already made up your mind on the presidential election, but if not I really hope you find the following useful.

Lies and the Lying Liars Who Tell Them

First, all politicians have some reputation for lying. This isn’t accidental, as stretching the truth is a commonplace strategy for convincing others to agree with one’s views. The website Politifact has sprung up in 2007 as a check against this phenomenon. They evaluate newsworthy quotes from politicians and score them on a scale that ranges from absolutely “True” to so inaccurate & ridiculous that it deserves to be called “Pants on Fire.”

Please, use this dashboard as a way to check your own assumptions about a politician’s honesty. It is tempting for most of us to focus on the truths told by the candidate we like and the the falsehoods spread by their opponents. However, we are more likely to learn new and interesting things about public figures by trying to stretch beyond our political echo chambers by also taking time to look at lies from those we admire and truths told by ‘enemies.’

While you do so, there are certainly on which you may question Politifact’s decision in ruling on a particular statement or in deciding which pronouncements they will attempt to judge in the first. Neither of these matters are without controversy, but it should be noted that 1) such critiques have come from both sides of the aisle and 2) they have won a Pulitzer prize for their work, which should not be taken lightly.

Publications’ Endorsements for POTUS

I also found a wonderful resource for newspapers’ Presidential endorsements, 1980-present. There have been some general trends, with publications leaning towards the Republicans in the 80s and moving left afterwards, but this year’s endorsements illustrate just how abnormal this election is.

You can use the menus at the bottom to highlight a particular paper, focus only on conservative papers (which seem to tell a particular interesting story in 2016), or choose to include 3rd party endorsements or papers’ abstentions. Regardless of the perspective you choose however, it is overwhelmingly clear that the press by and large see Trump as a truly abnormal and untrustworthy option. Some Trump supporters may see this as proof of a conspiracy against their candidate. From my point of view it is a reflection that those who have to pay close attention see him a dishonest con man whose greatest accomplishment has been inheriting nearly a quarter billion dollars and has not quite managed to squander said fortune.

Pokémon GO: Which of Your Critters Should You Use When Attacking a Gym? This Interactive Visualization Will Tell You

You’ve played Pokémon GO, and you’ve been confused. The game is addictive and fun, but it hardly explains itself. The confusion tends to be worst those who haven’t played previous Pokémon games, and it’s particular bewildering during gym fights. After reaching level 5, many players wander into a gym and pick a fight simply using their 6 highest-powered Pokémon, the game’s default. The experience can be frustrating and bewildering because A) the attacking and defending Pokémon’s types are enormous factors, and B) the actual battle mechanics are barely explained. The following visualization should help with A) and afterward I’ll recommend articles that speak to B).

The above will render differently on a laptop, tablet, or cell phone, and I would recommend that you use it first on laptop to customize it to reflect the best Pokémon you own, then refer to it via cell when you are out and about playing the game. As noted inside this visualization explain, there are four steps here.
1) Choose your best Pokémon. Open up the dropdown menu, click All twice to clear selections, then select the 6-10 best in your Pokédex. If you are still new to the game, you may notice that the full Pokédex is not available in the menus here. The reason for that ultra-common, unevolved Pokémon like Pidgey, Rattata, and the like shouldn’t be used for attacking gyms, and thus my data source didn’t bother to include their attacking stats. If none of your Pokémon are listed in the dropdown, then use gyms as an opportunity to get used to dodging, but focus most of your playing time on capturing critters and leveling up your trainer.
2) Select the Pokémon you’re facing or click on the Pokémon types. If you know the defender’s type, that will probably be faster. Usually gyms will have at least three defenders, and you will attack them with six. In that situation I would recommend making your first two good matchups vs. the first defender, your third and fourth solid opponents for the second defender, etc.
3) Check the chart to see who’s best vs. different types. You could skip step 2 and simply memorize the major strengths and weaknesses of you best critters in this chart. This chart could also be used to help you decide what to do with duplicate Pokémon that have different Fast and/or Special attacks. For example, in the following you can quickly see that a Dragonite with Dragon Breath is superior to one with Steel Wing and Dragon Claw is its ultimate Special move. Meanwhile, the best Blastoise have Water Gun for their Fast attack, but after that the Special attacks don’t make much difference (because Water Gun has a higher damage rate than any of the Special moves can).
drag & blast
4) Save your changes. You can access charts that reflect your changes on laptop or cell and it will render in a way that work well on that type of screen by default. The approach that works best for me is to select my Pokémon in Step 1) on my laptop, then click Share and copy the Link under Current View. To improve performance on cell phones, add the clause “&:showVizHome=no” at the end of the URL. By default Tableau, the software I created this in presents visualizations inside of a site that includes links to various other things, while “&:showVizHome=no” will let your browser render only the relevant chart, which is particularly useful on cell phone screens.
You can also go further once you have a Pokémon or two you’re fully happy with by clicking on the Fast &/or Special moves they don’t have and choosing Exclude. If you copy your Current View URL after taking this step, you’ll only see your Pokémon’s relevant moveset going forward.
arbok exclude

I hope you find this resource as useful as I am, but it only scratches the surface of options available to you that Pokémon GO doesn’t explain very clearly on its own. Here are a few articles and videos that I’ve found useful, in no particular order:
How to crush your opponents in Pokémon Go gym battles. Daily Dot’s article that brought this data source to my attention.
r/TheSilphRoad. The subreddit that the data set comes from. Lots of topics garner a lot of discussion here.
Silph Road’s Youtube Channel. There are a ton of video guides to various Pokémon GO right now, but I find this channel to be among the most useful, and while they don’t have a lot of clips yet, unlike others their vids tends to be brief, focused, and useful.
Subreddit discussion on dodging. A good guide to battle mechanics, in particular dodging, for after you’ve used my charts to select the best attacking matchups.

The Many Revolutions of Hamilton: An American Musical

Hamilton: An American Musical has redefined musical theater and dragged the public eye back toward the 250-year-old human drama of the American Revolution. It’s use of hip-hop to portray the revolution as a musical has led many people with interest in one of those spheres to pay attention to the other two.

Now this groundbreaking production enters an interesting phase as Miranda and others from the original cast move onto other opportunities and a touring production is kicking off as well. This seems like an appropriate time to step back and appreciate the history that fueled Miranda’s creation, and the original cast recording of the songs that propelled it to its immense popularity.

The above is intended to be a portal through which you can explore the lives of historical figures featured in Hamilton: An American Musical and key dates in Alexander Hamilton’s life, the Revolutionary War, and the early years of the Republic. After choosing a keydate, hovering over someone will tell you their birth and death dates, their age at the time of the chosen event (or how long since they died), and the bottom of the page will display a portrait of the historical figure and a picture of their original Broadway representation.

On top of that basic interactivity, clicking on a name will open that person’s Wikipedia page in another tab or window. Lin-Manuel Miranda has done a phenomenal job of dramatizing Hamilton’s life, but there are many fascinating events and facts that simply didn’t fit into one evening’s entertainment. For example:

  • Hamilton’s only offspring featured in the play is his son, Phillip, but he and Eliza had seven other children. If you follow Wikipedia links to some of their pages you’ll see that they had many accomplishments themselves, including Alexander Jr. serving as a divorce attorney for Aaron Burr’s ex-wife.
  • Aaron Burr’s grandfather was indeed a “fire & brimstone preacher,” perhaps the most infamous one of all, Jonathan Edwards who authored “Sinners in the Hands of an Angry God
  • John Laurens page is recommended reading top-to-bottom. A book, play, etc. focusing just on Laurens would be fascinating.

Miranda’s omissions of some of these facts and liberties taken for dramatic purposed have come under fire from some historians, but those critics seem to be missing the point. A musical is not and cannot treat a subject in the same way a history book does. The play does shine a light on a fascinating historical period that some had only looked at as a dry topic in history class. Hopefully the above makes it easier for people to see some of the larger trends in these figures’ lives and serves as a portal for deeper investigation.

Fueled by this history, Hamilton’s Original Broadway Cast Recording is engrossing on many other levels:

This Mental Floss article details the facts behind certain song lyrics, and can serve as a bridge between the history and the music of Hamilton.

Click on any of the song titles above will load a 30-second clip of that track from Amazon in a new browser tab or window. For obvious copyright reasons I couldn’t include the full songs in this way, but the link at the bottom of the screen takes you to the soundtrack’s page on Amazon, where you could purchase the soundtrack in full or Amazon Prime member can listen to it for free in its entirety.

Hovering over a track will load an illustration from a project called #Ham4Pamphlet inspired by the track. The project was organized by an artist named Arielle Jovellanos and along with being nice, fun art the illustrations often help those of us who haven’t had a chance to see the play live gain a better understanding of the visual context of the songs.

Lin-Manuel Miranda’s hyper-literate, polysyllabic rhymes as Hamilton, twisted Britpop romance in Jonathan Groff’s King George tracks, and incorporation of Revolutionary drinking songs are just a few of the fascinating lyrical, musical, and historical elements packed into this music.


I hope the above offers a fraction of the entertainment and insights I’ve gained while creating it and enjoying the soundtrack. This is my love letter to the play and my thank you to Lin-Manuel Miranda for creating the play and to Ron Chernow for writing the Hamilton biography that inspired Miranda.

Huge thanks to Jeffrey Shaffer, a true Tableau (the software used to create the above) master, who generously reached out after I tweeted an unspecific gripe about my difficulties in getting the songs to play in the second page. His identification of URLs for the Amazon song samples made that page possible.

All of the key date icons used in the first page come from The Noun Project, a great resource for iconography, free to use so long as the icons creators are credited. I used the following and am very thankful to the artists: white house by Luis Prado, Quill and Ink by Adam Terpening, Guillotine by Anton Gajdosik, Tombstone by Jeanette Clement, dollar by Christopher Beach, Military Rank by NAS, Cannon by Richard Dooley, and Hurricane by Lil Squid.

Salary Disparities Between and Within 2016 MLS Clubs

As in most sports leagues, MLS club payrolls vary greatly and the stars on any one roster make many times more than their youngest, least-proven teammates. You see similar dynamics in baseball, basketball, football, and hockey, but the scale of the issue is very different in those sports’ major American leagues, as their minimum salaries all hover around half a million dollars. Major League Soccer on the other hand has 52 of their 555 players making less than $60,000, and 124 making between $60k and $70k. While this is a marked improvement over David Beckham having teammates earning less than $20,000 (in Los Angeles!) when he entered the league in 2007, it’s still odd to see millionaires sharing locker rooms with guys making not a whole lot more than the Unite States’ national average wage.

There is a lot of interactivity built into the above chart, most of which I’ve tried to make intuitive, but I want to spell it all out here. Hover over a player for a summary of his wages and when click of him the league logo in the bottom right will become his club’s logo and the list that club’s total base salary and guaranteed compensation.
There’s a parameter under the chart’s title allowing you to switch between base salary and guaranteed compensation driving size, sorting, and coloration of the visualization. The color scheme, by the way, is there to reinforce the
Hover over the MLS Players Union logo and you’ll see they’re description of the dataset that feeds these charts. Click on it and you’ll be presented with a hyperlink to the data source.
Finally, and most subtly, you can right-click on any player and choose to exclude him. The effects of these changes will flow into the sorting of the chart of the league/club totals displayed at the bottom.

Beyond simply visualizing these disparities, salary data from the MLS Players Union reflected above are noteworthy for how little they drive results in this league. While most soccer leagues are oligarchical, dominated by and large by huge spenders with downmarket usurpers, like Leicester City in the 2015-16 Premier League, rare exceptions to the rule. Meanwhile, smart team-building is generally a more important factor than big spending in MLS, a trend which appears to be continuing in this young season, and FC Dallas, Real Salt Lake, and the San Jose Earthquakes are vying for the top spot in the league with below-average wages. I’ll likely revisit studies wages vs results studies later in the season (it’s far too early to say anything definitive based on 2016 results), and maybe look at impacts on attendance, too.

Role of Shot Location in Premier League Keepers’ Shot Stopping Ability. Interactive Viz of the Day

Basic goalkeeper statistics are too simple to be repeatable or useful. It is easy to look up a keeper’s save percentage or goals against average, but in the end those stats are so heavily reliant on quality of shots on goal faced that they are not a good indicator of keeping skill. Instead, let’s look at Premier League keepers in sample sizes that go beyond single seasons and focus on the role of shot locations, as well as those of the resultant saves and goals. The charts below default to all shots faced by keepers 2010-present in the English top tier. All hexagons are sized based on the volume of shots, saves, and goals. The visualization really comes alive once you click on either a) the name of a keeper to focus both charts on his shots faced and their outcomes and/or b) a zone in the top chart to see only save and goal locations below that came from shots that area.

What do we make of all this? First, while I’ll note some of my takeaways below, this dashboard is a representation of Opta data (mined by Paul Riley, @footballfactman on Twitter) containing over 150,000 shots of goal, and there are over 500 combinations of keeper and shot zone that can be selected here. I invite readers, especially those who have focused on a particular keeper’s club in the 2010s, to explore perspectives relevant to their interests and offer their own interpretations in the comments below, Twitter, reddit, or their own blogs. Please mention me on Twitter click to see my Twitter account for the last 3, as I really look forward to seeing others’ interpretations of this data. That said, here are my thoughts after sifting through these data while creating the dashboard:

1) A Keeper’s chart is an invitation for further investigation, not a final judgement.
While some keepers come across as more impressive (Adrian, with only one significantly below-average zone) than others (Wayne Hennessey, with 8 bad areas), it is probably more productive to view keepers in terms of opportunity for improvement and apparent relative strengths. For example, let’s compare some keepers in the middle range of the metric driving the bar chart on the left, Pepe Reina and Hugo Lloris:
Dashboard 2 (12)Dashboard 2 (11)
Here we have keepers with “bad” zones that never overlap. What do you make of these zones in which the keeper’s save percentage falls below average? Their own coaches might focus film study on those areas to see if the keeper has exhibited bad positioning tendencies that cost goals when facing shots from there (Reina in particular seems to have one particular angle to his left that was consistently problematic). If so, they can orient training around correction of the issue. Opponents might gameplan to specifically target the keeper’s “weaker” positions (Lloris is so strong near goal that Spurs opponents might not want to stress so much about working hard to create prime opportunities and instead let fly as soon they get an opening inside the box), though this could well be easier said than done.

2) When not shooting from straight on, far post shots seem to produce more goals.
While overall we see goals being scored in a largely symmetrical fashion, if you focus on shots to the left or right, keepers seem to have a harder time protecting the far post than the near post.
shots keeper's leftshots keeper's right
Note here that the data driving this presentation are limited to on-target attempts, so further study may be needed to discover whether strikers miss the goal mouth more often on far post shots than near post ones.

3) Major differences from the norm are particularly important.
On this topic I can’t help but pick on Wayne Hennessey, and by extension Crystal Palace.
The highlighted hex differences in the top chart are common among all keepers, as no one aligns very well with shot volumes or save percentages from all locations, but the differences in goal locations is quite unique to Hennessey and his club. Why is he allowing so many goals towards the center of the goal mouth? This data doesn’t include keeper positioning, speed of buildup, etc., so it’s hard to say if this trend is more a reflection of Hennessey errors, or his defense forcing him into extremely disadvantageous situations. As I mentioned earlier, sometimes exploration of these data mostly lead to asking a smarter, more focused question than providing irrefutable answers.

Again, do any patterns stick out to you while exploring these keeper trends? Insights from those who have followed a few seasons of a particular keeper’s club(s) or who want to delve into spinoff video analysis would be particularly interesting, as I have been looking at this from a much broader perspective. I’d love to read your thoughts in the comments below, Twitter, reddit, or your own site.

2016 Major League Baseball Team and Player Salaries, Visualized

Baseball has the best numbers. This is rather obvious when you look at the nature of the game in comparison to other team sports, as America’s Pastime is at its core a succession of individual pitcher-batter confrontations that are always carried out under extremely similar conditions. It’s a little more surprising that their salary data is better than other sports, though. USA Today has been compiling Major League Baseball salary data since 1988! In the last few years they have even begun to include the start and end date of a player’s current contract, with average salary over the life of that contract. In other sports leagues, salary data either gets leaked out by a players union then disputed by league officials or an enterprising journalist has to grill his contacts behind closed doors to get figures.

I took the 2016 MLB salary data from USA Today and charted them below. The boxes are all sized by current salary, while they are colored by average salary during the player’s current contract. When you see a small box with a dark shade of green, it means that either that this player will be paid much more in later years of his contract (i.e. Giancarlo Stanton of the Marlins) or is in the closing years of his contract with a salary that’s already falling a bit (the Yankees’ Alex Rodriguez is a good example). Hovering over any player will display a card with all of their contract details. The AL and NL tabs will give you a closer look within those leagues, and the Current vs. Average tab is a deeper dive into the relationship between current salaries and those laid out over the rest of the current contract. On any page, click on a team name or logo to go a tighter focus on their spending.

I tried to build interactivity into this so that baseball fans can dig into it however they see fit. I won’t expound on it all that much myself because as much as I enjoy baseball, I don’t dig into MLB anywhere near enough to feel like I can say anything terribly valuable about roster dynamics. Hopefully those that do have that knowledge will find the above tool useful for illustrating their thoughts on 2016 MLB salaries.

Fool’s Gold: But What If Klinsmann & The USMNT Had Lost?

After the US Men’s embarrassment in Guatemala last week, calls for the firing of Jurgen Klinsmann got, well just as fevered as they have been a few times during the last few years:

Sources have told me that US Soccer Federation held precautionary discussions around appropriate Klinsi replacements, and were ready to act had the team not rebounded on home soil. While that win on Tuesday saved Jurgen’s job for the time being, should the USMNT not show real improvement at Copa America, they will put the reigns in a manager’s hands currently in charge of a pro club here in the USA.
So, now we’re left with the hypothetical of what would have happened after a Tuesday loss or draw, and what might happen should the US lay an egg while hosting a significant tournament. Looking at the full US Soccer Pyramid, who would be most qualified? Opta data can provide us with a clear answer. There are a few simple statistics which we can collate into a single metric measuring coaching ability – this an assumption merely for the sake of this piece.
First, the USMNT needs a coach with motivational abilities, represented by distance covered, “r” in the equation.
Next, Opta passing statistics easily show us passing percentage allowing us to quantify ability to foster team chemistry, which “a” signifies.
To measure managers’ ability to inspire discipline, an inverse of fouls committed are signified by “d.”
We also need to account for defense, so blocked shots and saves are added up and signified by the single metric “o.”
Finally, the factor which pundits mention most often: possession, represented in our equation below as “m”)

The following dashboard visualizes the results. The word cloud and the bubble chart are the primary views, with a bar chart and pie graph illustrating the top 3 managers and each league’s total pool of coaching talent, respectively.

As you can see from the word cloud, Bruce Arena is clearly still the best manager in US Soccer.
Let’s just call him the Once & Future Boss. Colin Clarke and Oscar Pareja are the only ones that come close.
Most of the best managers are in MLS, as you would expect, but there are a few men in charge of lower-division sides, such as Clarke, who are more skilled than their top-tier comparables.
In fact, every tier has some coaches decidedly worse than those with the best BAPS scores in the tier just below them, and even some in the barely-professional leagues, PDL and NPSL, are better than Pablo Mastroeni. If MLS doesn’t start getting better managers soon, these superior lower-division managers, like Clarke, Sidd Finch, and Loof Lirpa, will upgrade their sides so much that the US Soccer cartel will no longer be able to avoid a structure of unlimited clubs in a promotion and relegation system.

Interactive Schedules for Every Club in the Top 3 US Soccer Leagues

When a sports league releases printed out schedules to their fans, it is generally condensed to a visual calendar that clearly and quickly conveys the full season at a glance. Unfortunately, online these same schedules tend to be organized as a long list that requires fans to scroll through or use control-F to find particular games they are interested in.

Below are the schedule for clubs within the top 3 soccer leagues in the US inspired by traditional schedule calendar handouts, but with the added interactive benefits in that hovering over an opponent will 1) highlight all other fixtures against that club and 2) pop up details about that specific fixtures. Major League Soccer kicks off this Sunday and the lower US soccer leagues, NASL and USL, also begin their seasons about a month from now.

Numbers to the left of every fixture represent day of the month.

Each league defaults to its 2015 champion. Use the navigator at the top to switch leagues and the dropdown above the league champion’s crest to switch the focus to a club of your choice. This format should make it easy to see periods that are particularly heavy or light on home fixtures. For example, don’t expect Toronto FC to start the MLS season strong, as their ninth match is the first one they will play at home:
That TFC roadtrip is pretty extreme, but it matches a general trend in MLS scheduling as they generally frontload early matches to be hosted mostly by clubs in the south, with those teams going on the road more often during the summer. It’s awkward, and it tends to make those clubs run hot and cold (pun intended) on predictable cycles. At the same time, this arrangement does have friendlier climates for matches as much as possible, which hopefully leads to more attractive play and higher tickets sales.

Feel free to take a screenshot, or generate an image using the Download link in the bottom right then save it for later reference or park it on your own blog. If you would really like an interactive version that defaults to a club of your choice, reach out to me on Twitter, @StatHunting. I could even quickly include a link an MLS club’s USL affiliate or vice versa.

On the fourth page above there’s also a chart tracking fixtures per month for all 3 leagues (regular seasons only). The general order of fixture congestion makes sense because of the size of these leagues, as USL has 31 clubs, MLS has 20, and NASL will start its season with 10 and end with 11. You can also see that MLS and NASL have opted to avoid to overlapping their fixtures with the high-profile Copa America and Euro tournaments in June, while USL will barrel through, scheduling 65 matches during that month.

I hope you find these useful. As much as I travel for business, I will definitely be using them to quickly figure out if there’s a match happening while I’m visiting a particular city. If you have any questions about the calendars, particularly how I built them in Tableau, please reach out to me on the comments or (for a quicker response) on Twitter.

Making Sense of the Ever-Changing USL and its Relationship with MLS

As the largest league in US Soccer and functioning partially as a farm system for MLS, United Soccer League takes up an intriguing space in the domestic game. MLS clubs either set up their own developmental club or loan some of their players to a USL affiliate, making the lower league a preview of the top tier’s younger players. It also serves as an organizational preview, since the 29-club USL is facing scheduling challenges that MLS will likely face if they continue their current pace of expansion. Whether or not MLS is guiding USL on scheduling, they are likely gauging fan reactions to better anticipate their own course when they get than large.

The USL has nearly tripled in size during the last few years, and it can be hard to keep track of league-wide trends, even for fans of a specific USL club. The league can seem even more daunting for outsiders, like MLS supporters who want to keep tabs on their club’s USL affiliate. I’ve made the following visual guide to USL Conferences and clubs’ MLS affiliates.

The above project started as my attempt to improve upon a map USL released yesterday to illustrate their new conference alignment. It was quickly criticized on Twitter for being confusing, and from my perspective it was undone by arbitrary coloration of states. For my version I started with a USL wiki page that lists city, MLS affiliate, etc. for every USL club, then adding the names of every other US state and Canadian province to the dataset before loading it into Tableau. To define conference for all states and provinces (even those without a team) I used rectangular selection on the map. Then I drove coloration of them based on conference and set lighter hues for those that don’t have a club right now.

I hope you find this useful. If you have questions about the dashboard, or how I developed it in Tableau, please leave them in the comments section. If run a soccer-themed site or blog where this visual guide to the USL would be helpful to your readers, click Share in the bottom right of the dashboard, copy the embed code, and post it into the HTML input for your own post. Then add a comment with a link to the location of the post.

Steph Curry’s Unbelievably Effective Shooting

Steph Curry is currently having the best shooting season the NBA has seen in 30 years. Throughout NBA history, the only players who ever had season with Effective Field Goal Percentages in his current range were Wilt Chamberlain and Artis Gilmore, 7-footers who played in eras in which they were rarely defended by someone their size. His shot volume is unprecedented, too. The record for 3-pointers per game is Baron Davis in 2004-05 with 8.7, but Curry is launching 10.6 per game, and while Davis and most others in the top 10 weren’t making enough to justify that rate, there are arguments to be that Curry should shoot even more often.

I wanted to see what this actually looked like in terms of Curry’s shot locations and how far into the shot clock he’s shooting. So, I plugged his stats into Tableau and came up with a hex plot that bins his attempts into areas on the floor, as well as other charts around general shot ranges and seconds left on the shot clock. By default, none of the charts will show a data point in which less than 4 shots have been taken, but you can change that in the bottom left:

Note that you can click on a shot range or shot clock times to filter the hex plot of shot locations. For example, if you hold down shift and select all shots taken within the first half of the shot clock (66% of his shots), you get the following result:
curry within 12 seconds
If you get too specific in those selections, you may want to change the Minimum Shots threshold set in the bottom left, as you’ll find few areas of the court with 4+ shots taken if you’re narrowing the focus to only 3-pointers taken with 0-2 seconds left on the shot clock.

While there are a few areas on the floor in which Curry’s Effective Field Goal Percentage falls below 50% (roughly the overall league average, and the midpoint of the color scheme on all the charts), the overall picture is almost unbelievable, as 75% of the data points represent an Effective FG% above 50, many of them well beyond 60% or even 70%. That’s absurd.

Effective Field Goal Percentages is more useful than standard Field Goal Percentage in conveying Curry’s accomplishments because it normalizes shots based on how many points they are worth. Shots from three point have a 150% the contribution of other shots in the calculation of Effective FG% just as they do in the game. Without that adjustment, field goal percentage looks markedly worse for players who routinely attempt threes, even though a good distance shooter is an immensely valuable commodity.

The other interesting aspect I alluded to before is the layout of Curry’s shots within the shot clock. As the primary shooter in an offense designed to get good shots off quickly, it is remarkable that 77% of his attempts came before the shot clock reached single digits. I do wish I had league-wide shot clock/location stats as points of comparison, but even without those figures it’s obvious that we are witnessing a truly remarkable season from Steph Curry right now.