Tiger vs Jordan vs Rory – Who’s Best at Their “Best”

A question that we often ask ourselves while we watch a player trample over a field on their way to victory is ‘can this guy ever lose if he plays his best?’

Well I am here to look into this question by comparing Tiger Woods, Jordan Spieth, and Rory McIlroy to each other during their ‘best year.’ For Tiger, I chose 2007, since he won about 45 times. For Jordan, I (obviously) chose 2015, where he won 2 majors and the FedEx Cup. Lastly, for our recently controversial Rory, I chose 2014, when he dismantled major fields with his Driving.


Okay, so first let’s look at driving stats during their ‘good’ years. The first plot is a distribution of drives for each player.

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Not surprisingly, Rory’s curve is furthest to the right, meaning his driving average of 305 yards is the longest among the 3. Next is Tig, who averaged 295 off the tee. Last is Baby Spieth, at 289 yards. Also interesting is the shape of the curve. Tiger and Rory have a larger standard deviation, which means their drives had more variation in distances. This perhaps indicates that Tiger and Rory hit a larger variety of clubs off the tee, or perhaps that they each have an ‘extra gear’ with their driver. Jordan has a much narrow curve, indicating a smaller standard deviation. Since he hits it shorter it is not surprising he doesn’t have a lot of variation off the tee in terms of distance.

Of course we cannot just look at distance, we also need to look at accuracy. Unfortunately, way back in 2007, they did not as much data, so I can only compare fairways hit data. I would have liked to look at distance from middle, but what the hell..here is the data below:

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It looks like we would expect. Rory, who had the longest driving average, has lowest accuracy. Conversely, Jordo is the most accurate. Worth noting that there is actually not much variation here, I had to zoom in to make the graph look interesting. Only 4% separates Jordan and Rory, which is about 0.5 fairways a round. So while Jordan gives up 15 yards on average, he only makes up for it by hitting 0.5 fairways more each round. And Tiger is sitting in the middle again.

From the data, it looks like Rors is the best driver of the ball. Not to mention, when he misses, it isn’t by much (unlike Tig). It would have been interesting to see how much Tiger misses by on average, but recall the data is not available back then. Nonetheless, Rory is pumping it 10 and 15 yards past the two others on average, and not losing that much in accuracy.

So this round goes to RORY!!


Okay, approach shots. In my opinion, I think all these guys are among the greatest in terms of mid-long irons when they are on their games. Maybe include Phil in there as well. Below I simply plotted a bar graph which indicates the average distance from the pin each player hits it from a variety of yardages. Here it is:

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Oh boy, let’s see here. Tiger is the red (of course), he has the lowest distance to pin in 5 of the 8 categories. He really makes his hay from 150 to 200 yards, hitting it 4 – 5 feet closer ON AVERAGE than the other two. And his wedge game (50 – 100) is much more accurate than the other two. Jordan is the most accurate from 200-225 yards, but there probably isn’t much of a sample size there. Tiger is the worst from 25 – 50 yards, which is strange (maybe yips were always there). Look at this where it interests you and how you want, but the takeaway is that Tiger is a man among boys when it comes to hitting towering iron shots.

This round goes to…the TIG!!



Alright baby Jordan, let’s give you your bottle. Despite how great Tiger was/is on the greens, I do not expect him to challenge Jordan in this category. I did the same sort of analysis as for the approach shots, but for putting. Here it is:

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So from 2 – 5 feet, there is not much difference, thought Rory might be lacking here but no (this year (2016) would be different, remember this is 2014 Rory). From 5 – 10 feet, we see Tig fall off the pace a little bit, making 5% less of his putts than the other two. Tiger makes a statement from 10 – 15 feet, making 4% more than the others. After this, we see that those images of Jordan rolling in 20 footer on top of 2o footer does in fact translate to the data. He only makes 2% less of his putts from 15 – 20 feet than he does from 10 – 15 (is that good or bad..?). From 20 – 25 feet is just absurd, he makes 12% more than Tiger, and an astounding 18% more than Rory. Remember people, it is not uncommon to have putts from 15 – 25 feet, and he is lapping TIGER WOODS (the GOAT) here!

Alright Jordan…you got this round..



So who is better…I DON’T KNOW!
I ignored scrambling stats, would have liked to see approach shots from the rough or scrambling percentage. But the main categories of golf are well represented here. Something else to consider is that Tiger and Rory played much less than Spieth, so Jordan had access to ‘easier’ events probably. And very important to consider, because this is PGA Tour data, this does not include Augusta, US Open, or British (I think, definitely not Augusta). Can’t help but feel including Augusta would have helped Jordan’s case here.

They each won one category, and we don’t know which category is most important. Nobody is significantly lacking in any category, whatever Jordan is losing off the tee he appears to be making up for by raining in bombs on the greens, Rory wasn’t as bad at putting as I initially thought. Tiger wasn’t the worst in any category, and won one, unlike the other two, who each came last in a category. Tiger was definitely more superior to his peers than the others, but in terms of stats, it is not as clear he was any better.

The days of Tiger playing like shit and winning may be over if he comes back. According to these stats, he’ll have to play the lights out to win, which is the norm now.

Thanks to PGATour.com for the data..

Match Play – Data Exploration Exercise

It has never been easier to access and analyze PGA Tour golf stats. BUT, despite the abundance of stroke play data, there is no data readily available for match play analysis. Considering all the analysis that goes into the Ryder Cup, Presidents Cup, and WGC Match Play, there should at least be SOMEBODY out there analyzing some real data, instead of Brandel just saying that Jimmy Walker will win a match because he rotates through the ball well (even though he does). My discontent was so strong that I have actually taken the time to develop a web scraping script that extracts useful information on Match Play data back to 2008. The match play data is pulled off of golfchannel.com, and the relevant player data (strokes gained, world rank) is taken off of pgatour.com.

In the end I was able to assemble my own dataset by using a various number of data structures throughout the process. If your interested, I used the Beautiful Soup, pandas, and numPy libraries in Python. I have also attached all the code right here in zip file:

Python Scripts: Match_Play_Code

I was able to scrape the following information off of the internet using the above script for every match played from 2008 to 2016 at the WGC Match Play.

  • year match took place
  • name of each player involved in match
  • nationality of each player involved
  • world ranking of each player
  • strokes gained putting for each player at time of match
  • strokes gained driving for each player at time of match
  • and of course, who won the matchOkay, so finally we have some data that may give us some new insights on the mysterious world of match play.


    The rest of this post is going to introduce us visually to the never-viewed Match Play Data.

    So let’s get into it. First off, I am going to take a deeper look at Strokes Gained and see if it plays a role in predicting who will win the match. More interestingly, what do you think will be the stronger predictor, strokes gained putting, or strokes gained driving? I think it will be driving, just because I don’t know how you can play against a guy like DJ hitting it 350 down the middle every hole and not get demoralized, even if your Jordan Spieth.

    Below, I have created a figure that shows the difference between strokes gained putting on the x-axis, and the difference between strokes gained driving on the y-axis, each point corresponding to a specific match. A red x indicates a match that was lost by the player whose stats were being subtracted from. A green circle, as you probably guessed, means that the player whose perspective the axis are from, won the match. I also quickly did a logistic regression, and plotted the linear decision boundary on the plot. A point that lies on the decision boundary is classified as a “toss-up” by the algorithm, meaning that there is a 50% chance either player wins. Points above are predicted to be ‘wins,’ while points below the line are predicted ‘losses.’

    Screen Shot 2016-07-05 at 4.49.55 PM
    We do see that strokes gained (putting or driving) is associated with winning. There are a lot more green dots than red in the top right of the figure, and more red crosses than green in the bottom left. The decision boundary matches our intuition nicely. Our algorithm predicts that if a player has a higher strokes gained putting and strokes gained driving (or any combination that lands itself above the blue line), he is most likely to win the match. But which one is more associated with winning? Driving or putting? Well, the slope of the decision boundary is about -1.2. Suggesting that Strokes Gained Putting is slightly more important in deciding the outcome of the match. The idea being that a completely vertical line would indicate that only putting decides the outcome. Of course this is a backhanded analysis, but it will have to suffice.

Okay so strokes gained looks like it is a decent candidate for being a predictor, however World Rank is the one I think I would ultimately trust if I had to bet on a match. So below, I tried to visually assess if win probability is an increasing function of how far ahead you are in the OWGR (Official World Golf Ranking). So I divided the difference in world rankings in each match into various bins, which are on the x-axis. The height of each bar tells you what percentage of the time the higher ranked player won the match for that bin. For example, in matches where the world ranking separation was between 0 and 4 (first bin), the higher ranked player won 49% of the time. When the ranking difference was between 50 and 54, the higher ranked player won 71% of the time.

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Another way of thinking of this is the percentage of time that basing a prediction solely on world ranking points will give you the accurate prediction.
There is an upward trend in the data. As the difference in world rankings becomes larger, the percentage of time the higher ranked player wins also increases (usually). The exception is clearly the second last bin (please contact me if you can give me a reason). In case you are wondering, all of these bins have a sufficiently large and consistent amount of data. Despite the increasing trend, being over 60 world ranking positions ahead of someone only gives you a 70% chance of winning. Given that a 1 seed has never lost in NCAA Tournament, golf is looking pretty random. A more interactive version of this figure is located at this link.

Moving on…
Since this is a Match Play analysis, we need to at least address the Ryder Cup. We all know that the Europeans are “great” match play players, so they say. To shoot this idea down I just quickly looked at the track record of USA vs EUROPE from the past 8 years to see if this perception holds true in the WGC too.

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Well there you have it. This only leaves two possibilities

  1. The Ryder Cup is random,
  2. or the Euros are only good at ‘team’ match play and/or the US especially suck at team match play. (Tiger and Phil…)

tig and phil

Okay, so last thing we are going to look at is simply who is the BEST match play player over the past 8 years. The figure below simply plots wins against losses. Only players who have completed at least 5 matches were included. Some of the points are labelled, to get interactive version click on this link!
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As expected, we see the top guys fairly low and to the right, indicating lots of wins relative to losses (the darker the dot, the higher the win percentage). Some notables:

  • Rory McIlroy –> 22 – 8
  • Jason Day –> 21 – 6
  • Adam Scott –> 3 – 10   WOW!!
  • Jordan Spieth –> 8 – 3
  • Dustin Johnson –> 8 – 10
  • Rickie Fowler –> 10 – 6
  • Bubba Watson –> 12 – 7  *I thought he was “too nice” for match play*

Again, hit this link if you want to explore the data yourself. I really recommend it.


I did actually cut the data in half and trained a full (using all features) logistic regression on the data and tried to predict out of sample. The model managed to predict the outcomes of 60% of the matches correctly. Over 18 holes of golf I’m not sure it is possible to do much better without monitoring closely how well a player is playing leading into the event…hm.