What Tennis Can Teach You About Social Media Metrics

| November 9, 2011 | 3 Comments

by Robert Madison, Spiral16

How many “Likes” does it take to win Wimbledon? Or, why stats and reporting aren’t the same thing as social media analysis, insight, and understanding.

You can know my blood pressure, my weight, my IQ, etc … but none of those things will tell you that I’m a jerk. Now, spend 5 minutes with me… [laughter from the crowd]” ~ Jay Samit @ PivotCon 2011

In doing web analytics and social media analysis using digital and social media metrics, you often find yourself asking this question: What do they actually mean?

I submit under the “Thank You, Captain Obvious” category that the reporting of stats and numbers is useless without an understanding of what those stats and numbers mean and how they relate to one another. Likes, followers, tweets, fans … maybe they mean something, maybe they don’t. So how do you know which social media metrics matter and which ones don’t?

In part, this is an easy question to answer: If you don’t know what the stats mean, they don’t mean anything …

To illustrate this point, I’m going to switch from likes, followers, tweets, fans, and other social media metrics to something a little more established: The statistics of tennis. What stat(s) matter most in tennis, and what should you be monitoring to see if you’re going to have a successful outcome? Surely, we know exactly which stats matter the most after more than 100 years of playing the game.

So let’s rewind the clock to July 2008. Wimbledon. Roger Federer, then the world’s number-one tennis player and five-time defending champion was denied a record sixth consecutive title by Rafael Nadal, then the world’s number-two ranked tennis player in what John McEnroe called the “greatest match ever played.” I watched the match and even though I’m a die-hard Federer fan, I’ll concede that McEnroe at least makes a compelling argument. It was a really, really good match, painful as that is for me to admit.

So how did Nadal win? How did he beat the number-one player in the world? For those of you who didn’t watch the match (for shame!), here’s what it looks like on paper:

Statistics reveal how Rafael Nadal beat Roger Federer: 6-4 6-4 6-7 6-7 9-7

Federer v Nadal

1st Serve % 128 of 195 – 66 % 159 of 218 – 73 %

Aces 25 6

Double Faults 2 3

Unforced Errors 52 27

Winning % on 1st Serve 93 of 128 – 73 % 110 of 159 – 69 %

Winning % on 2nd Serve 38 of 67 – 57 % 35 of 59 – 59 %

Receiving

Points Won 73 of 218 – 33 % 64 of 195 – 33 %

Break Point Conversions 1 of 13 – 8 % 4 of 13 – 31 %

Net Approaches 42 of 75 – 56 % 22 of 31 – 71 %

Total Points Won 204 209

Fastest Serve 129 MPH 120 MPH

Average 1st Serve Speed 117 MPH 112 MPH

Average 2nd Serve Speed 100 MPH 93 MPH

Match time Four hours 48 minutes

Uh oh..I can tell by the look on your face; you’re confused. This, incidentally, is the problem with reporting without analysis; of being presented with a bunch of numbers without context and/or understanding. What’s the story behind the numbers?

OK, well let’s try this: In sports, it’s a pretty safe bet that the team with the most points wins. Nadal scored more points than Federer (209 to 204, respectively), and Nadal won the match, so let’s use more points as the key metric for what it takes to win a tennis match.

Wimbledon | July 2010

Player A

Player B

Total Points

478

502

Winners

246

244

Unforced Errors

52

39

 

OK, so here we have another tennis match to look at. I’ve already done you a big favor by eliminating a lot of the less important (not unimportant, just less important) statistics. Plus, I’ve cleaned up the presentation of the data. Yes, it matters.

As you can see, Player B scored more points than Player A, so using our “more points” rule, we can infer that Player B won this match. Except that we’d be wrong. Unfortunately for Player B, the history books didn’t record it that way and Nicholas Mahut (Player B) lost to John Isner (Player A) in the longest tennis match ever played. (11 hrs, 5 min).

So what gives? The player with the most points lost? Weird. Well, maybe it was winners? Player A hit more winners than Player B (246 to 244 respectively), so maybe it’s the player with the most winners that wins a tennis match? Let’s use more winners as the new rule.

Agnieszka Radwanska vs Andrea Petkovic Beijing 2011 Final

Radwanska

Petkovic

Total Points

100

101

Winners

32

54

Unforced Errors

12

33

 

Not only did Petkovic score more points than Radwanska, she also hit more winners! There can be little doubt that Petkovic should have won this match. And she should have. But she didn’t.

What’s going on here? More points doesn’t always win, more winners doesn’t always win… maybe it was unforced errors? Maybe the player with the fewest unforced errors wins matches? Let’s use that as the new rule.

Nalbandian vs Hewitt | Australian Open | Jan 2011

Nalbandian

Hewitt

Total Points

193

193

Winners

65

53

Unforced Errors

83

62

 

We’ve already seen that the player with the most points doesn’t always win, and it wouldn’t matter here anyway since they both scored the same amount of points. And we’ve also seen that the player who hits the most winners doesn’t always win. So now we’re thinking that the player with the fewest unforced errors is the one who will win the match.

Looking at the same statistics and measuring the same social media metrics won’t make you a winner every time.

Generally speaking, that’s a pretty safe guess but it’d be wrong in this case as Lleyton Hewitt lost the match despite hitting 21 fewer unforced errors and scoring just as many points!

So what can this teach you about business, web analytics, and social media metrics?

Lesson #1: In business, as in tennis, what it takes to win isn’t always the same thing for every company or for every situation. Sometimes it’s hitting more winners. Sometimes it’s doing social media analysis and tracking sentiment over time to weed out problems before they take root. Sometimes it’s making fewer mistakes than your competition. Regardless, it’s almost always going to be a combination of things; very seldom will it be just one magic answer.

Lesson #2: And sometimes it doesn’t matter. If your product or service (and message about same) simply isn’t as good as your competition, the best social media analysis/marketing strategy in the world won’t save you. This is something I don’t see stressed often enough. For example, everybody talks about how Ford uses social media to increase sales, but let’s not forget that Ford had to build a great product first. Social media allowed people to share their thoughts and experiences about Ford, and because the product(s) were great, the ensuing conversation was positive. If the product(s) hadn’t been great, the conversation would have reflected this, and Ford wouldn’t be proud of it. The importance of this cannot be overstated.

Lesson #3: Social media analysis, simply put, cannot be automated. Q.E.D.

About Robert Madison

Robert works for Spiral16, a social media monitoring company and – when not working, being a husband, or being a father – plays as much tennis and/or guitar as possible. He’s also oddly proud of having created the “Bracket of Death”, an event best described as “part tennis tournament, part endurance contest.”

Category: Digital Marketing Guest Posts, Social Media Marketing, TMMPDX

admin Digital Marketing Guest PostsSocial Media MarketingTMMPDX

by Robert Madison, Spiral16

How many “Likes” does it take to win Wimbledon? Or, why stats and reporting aren’t the same thing as social media analysis, insight, and understanding.

You can know my blood pressure, my weight, my IQ, etc … but none of those things will tell you that I’m a jerk. Now, spend 5 minutes with me… [laughter from the crowd]” ~ Jay Samit @ PivotCon 2011

In doing web analytics and social media analysis using digital and social media metrics, you often find yourself asking this question: What do they actually mean?

I submit under the “Thank You, Captain Obvious” category that the reporting of stats and numbers is useless without an understanding of what those stats and numbers mean and how they relate to one another. Likes, followers, tweets, fans … maybe they mean something, maybe they don’t. So how do you know which social media metrics matter and which ones don’t?

In part, this is an easy question to answer: If you don’t know what the stats mean, they don’t mean anything …

To illustrate this point, I’m going to switch from likes, followers, tweets, fans, and other social media metrics to something a little more established: The statistics of tennis. What stat(s) matter most in tennis, and what should you be monitoring to see if you’re going to have a successful outcome? Surely, we know exactly which stats matter the most after more than 100 years of playing the game.

So let’s rewind the clock to July 2008. Wimbledon. Roger Federer, then the world’s number-one tennis player and five-time defending champion was denied a record sixth consecutive title by Rafael Nadal, then the world’s number-two ranked tennis player in what John McEnroe called the “greatest match ever played.” I watched the match and even though I’m a die-hard Federer fan, I’ll concede that McEnroe at least makes a compelling argument. It was a really, really good match, painful as that is for me to admit.

So how did Nadal win? How did he beat the number-one player in the world? For those of you who didn’t watch the match (for shame!), here’s what it looks like on paper:

Statistics reveal how Rafael Nadal beat Roger Federer: 6-4 6-4 6-7 6-7 9-7

Federer v Nadal

1st Serve % 128 of 195 – 66 % 159 of 218 – 73 %

Aces 25 6

Double Faults 2 3

Unforced Errors 52 27

Winning % on 1st Serve 93 of 128 – 73 % 110 of 159 – 69 %

Winning % on 2nd Serve 38 of 67 – 57 % 35 of 59 – 59 %

Receiving

Points Won 73 of 218 – 33 % 64 of 195 – 33 %

Break Point Conversions 1 of 13 – 8 % 4 of 13 – 31 %

Net Approaches 42 of 75 – 56 % 22 of 31 – 71 %

Total Points Won 204 209

Fastest Serve 129 MPH 120 MPH

Average 1st Serve Speed 117 MPH 112 MPH

Average 2nd Serve Speed 100 MPH 93 MPH

Match time Four hours 48 minutes

Uh oh..I can tell by the look on your face; you’re confused. This, incidentally, is the problem with reporting without analysis; of being presented with a bunch of numbers without context and/or understanding. What’s the story behind the numbers?

OK, well let’s try this: In sports, it’s a pretty safe bet that the team with the most points wins. Nadal scored more points than Federer (209 to 204, respectively), and Nadal won the match, so let’s use more points as the key metric for what it takes to win a tennis match.

Wimbledon | July 2010

Player A

Player B

Total Points

478

502

Winners

246

244

Unforced Errors

52

39

 

OK, so here we have another tennis match to look at. I’ve already done you a big favor by eliminating a lot of the less important (not unimportant, just less important) statistics. Plus, I’ve cleaned up the presentation of the data. Yes, it matters.

As you can see, Player B scored more points than Player A, so using our “more points” rule, we can infer that Player B won this match. Except that we’d be wrong. Unfortunately for Player B, the history books didn’t record it that way and Nicholas Mahut (Player B) lost to John Isner (Player A) in the longest tennis match ever played. (11 hrs, 5 min).

So what gives? The player with the most points lost? Weird. Well, maybe it was winners? Player A hit more winners than Player B (246 to 244 respectively), so maybe it’s the player with the most winners that wins a tennis match? Let’s use more winners as the new rule.

Agnieszka Radwanska vs Andrea Petkovic Beijing 2011 Final

Radwanska

Petkovic

Total Points

100

101

Winners

32

54

Unforced Errors

12

33

 

Not only did Petkovic score more points than Radwanska, she also hit more winners! There can be little doubt that Petkovic should have won this match. And she should have. But she didn’t.

What’s going on here? More points doesn’t always win, more winners doesn’t always win… maybe it was unforced errors? Maybe the player with the fewest unforced errors wins matches? Let’s use that as the new rule.

Nalbandian vs Hewitt | Australian Open | Jan 2011

Nalbandian

Hewitt

Total Points

193

193

Winners

65

53

Unforced Errors

83

62

 

We’ve already seen that the player with the most points doesn’t always win, and it wouldn’t matter here anyway since they both scored the same amount of points. And we’ve also seen that the player who hits the most winners doesn’t always win. So now we’re thinking that the player with the fewest unforced errors is the one who will win the match.

Looking at the same statistics and measuring the same social media metrics won’t make you a winner every time.

Generally speaking, that’s a pretty safe guess but it’d be wrong in this case as Lleyton Hewitt lost the match despite hitting 21 fewer unforced errors and scoring just as many points!

So what can this teach you about business, web analytics, and social media metrics?

Lesson #1: In business, as in tennis, what it takes to win isn’t always the same thing for every company or for every situation. Sometimes it’s hitting more winners. Sometimes it’s doing social media analysis and tracking sentiment over time to weed out problems before they take root. Sometimes it’s making fewer mistakes than your competition. Regardless, it’s almost always going to be a combination of things; very seldom will it be just one magic answer.

Lesson #2: And sometimes it doesn’t matter. If your product or service (and message about same) simply isn’t as good as your competition, the best social media analysis/marketing strategy in the world won’t save you. This is something I don’t see stressed often enough. For example, everybody talks about how Ford uses social media to increase sales, but let’s not forget that Ford had to build a great product first. Social media allowed people to share their thoughts and experiences about Ford, and because the product(s) were great, the ensuing conversation was positive. If the product(s) hadn’t been great, the conversation would have reflected this, and Ford wouldn’t be proud of it. The importance of this cannot be overstated.

Lesson #3: Social media analysis, simply put, cannot be automated. Q.E.D.

About Robert Madison

Robert works for Spiral16, a social media monitoring company and – when not working, being a husband, or being a father – plays as much tennis and/or guitar as possible. He’s also oddly proud of having created the “Bracket of Death”, an event best described as “part tennis tournament, part endurance contest.”