Another Week, Another Set of Live Rounds of “The Voice”

This week on “The Voice,” we’re down to 10 contestants, another two of whom go home tonight, so if nothing else our odds of predicting who’s going home are naturally improving week to week. But let’s look at the social media numbers again. First, overall “share of voice”:

thevoice-overall 2013-05-21 at 10.07.39 AM

Then, positive “share of voice”:

thevoice-positive 2013-05-21 at 10.07.19 AM

For the first week ever, both rankings produce the same bottom two, Amber and Kris.

If we look at week-to-week follower count growth, however, we get a different ranking:

Twitter handle Followers 5/14 Followers 5/21 Delta Percentage
@theswonbrothers 14750 19588 4838 33%
@michellechamuel 21305 27370 6065 28%
@josiahhawley 40894 50517 9623 24%
@dbradbery 45723 55430 9707 21%
@hollytmusic 13454 16273 2819 21%
@ambercarrington 14520 17149 2629 18%
@kristhomasmusik 12130 13966 1836 15%
@sarahsimmusic 25314 28828 3514 14%
@sashaallenmusic 16674 18985 2311 14%
@judith_hill 39953 43041 3088 8%

So the question is whether buzz or follower count (or neither) correlates better with actual results. So far, follower count has correlated a lot less well, so this week I’m going with buzz — especially since overall buzz and positive buzz line up this time.

But social numbers haven’t been right yet this season, which I’m sure make Kris and Amber happy to no end. 🙂

What Did We Learn from This Week’s “The Voice”?

Yesterday I looked at a number of different cuts of social media data and found a number of candidates for the Bottom 2 on “The Voice.” The interesting thing about the actual results was that none of the data I had access to correlated with the outcome, at least in a discernable pattern that you could build any sort of rule or model off of. I could try using a stats tool like R to find non-intuitive features that better predict an outcome, but that is probably like using a bulldozer to pound in a nail — there aren’t enough samples to make that approach meaningful. So I’m left with hypotheses:

  • Twitter isn’t a good proxy for voting behavior in the case of this show due to either a demographic skew or to the fact that the audience gravitates in general to other channels of interaction.
  • I haven’t found the right features yet. I used growth in Twitter followers as one potential proxy for “momentum,” and that correlated with one of the results — Vedo had by far the lowest percentage growth in followers week to week (he started of course with the highest number, so it gets harder to maintain the same growth rate), but in the case of the other ousted contestant, Garrett Gardner, we saw the highest percentage growth in followers, so that piece of data doesn’t correlate at all. Garrett, in fact, by any of the numbers I had access to or derived, should still be on the show. (Though I personally found his vocal performance on Monday weak, despite an interesting arrangement.)
  • The vote tallies among the bottom half of the contestants are actually fairly close (which the counts I had showed as well), so until we get to a point where we see either much greater volume or much greater differences between contestants, the best we’re going to do is random guesses over a larger pool of contestants with similar numbers. In other words, absolute rankings don’t work until you start to see larger gaps.

One thing is for sure — pure follower count is meaningless in this show (as it was in last week’s “American Idol”). A lesson for all who use follower count as a proxy for influence.

Social Media Numbers for “The Voice” Top 12

Last night’s “Voice” episode featured the Top 12, and for the first time this season, audience votes and downloads are the only determining factor for who goes home tonight. Like last week, I’ll be looking at Twitter numbers as a proxy for voting behavior so that we can see the extent to which opinions expressed there correlate with results. The voting methods as explained on the show were phone, text, Facebook and iTunes download, so Twitter is an independent factor in this equation and has no direct bearing on the outcome.

Last week, the Twitter-derived numbers correlated reasonably well. Not perfectly, though. For Team Adam, I hadn’t set up the topic in time, so had about 1/5 of the numbers I had for the Team Blake and Team Shakira contestants. So there my numbers were just plain off. The other factor was the judge’s discretion, and Blake elected to save the Swon Brothers rather than Justin Rivers. Welcome to the big leagues!

I personally enjoyed many of last night’s performances, but what’s important is what those who might vote thought. I took a couple of different cuts at the data. The first is simply looking at “share of voice” from the period of the airing of the show (starting in the Eastern time zone) through late this morning Pacific time. Here’s what we get:


Simply taking the ranking from most mentions to least during this time period, we end up with a Bottom 2 of Kris Thomas and Michelle Chamuel. We could look at the data another way, though: if we filter by positive sentiment — the idea being that people who express positive opinions are more likely to vote (or perhaps “you vote FOR someone, not AGAINST someone else”) — then the rankings change a little:



Now Amber Carrington and Holly Tucker make up the Bottom 2. However, note that the overall numbers aren’t very high when we apply this filter (sentiment analysis isn’t an exact science by any means, and the software used here is biased towards precision over recall, so is somewhat conservative.

Confounding the data further is iTunes, which doesn’t give precise data but does provide a “popularity” meter. If we look at each of the above sets of Bottom 2, we do see that Amber and Michelle’s performances did not max out the popularity meter, whereas Kris’ and Holly’s did.

One other data point we can look at is a week-to-week difference in Twitter followers. If we rank by percentage gain first, then by overall number, we get:

Twitter handle FollowersLastWeek FollowersThisWeek Delta Percentage
@garrettgardner2 16893 24426 7533 45%
@michellechamuel 14872 21305 6433 43%
@dbradbery 32352 45723 13371 41%
@josiahhawley 29100 40894 11794 41%
@theswonbrothers 10644 14750 4106 39%
@hollytmusic 10232 13454 3222 31%
@ambercarrington 11274 14520 3246 29%
@kristhomasmusik 9721 12130 2409 25%
@sarahsimmusic 20826 25314 4488 22%
@sashaallenmusic 13735 16674 2939 21%
@judith_hill 36078 39953 3875 11%
@vedothesinger 59217 61606 2389 4%

Based on this, it looks like Michelle picked up a lot of followers, so I’m going to have to go with Kris and Amber as the ones going home, with a possibility that Holly ends up somewhere in there. But I’m not taking it to Vegas — the differences aren’t large. Tough call this week!

Enjoy the show!

It’s Finals Season Once Again — Prediction Time for “The Voice”

I was planning to enjoy a few months as a professional triathlete — well, that’s what my wife calls my vacation from work — but a little issue somewhere in the left glute / piriformis / hamstring has curtailed my running, so I find myself back in the world of social analytics, but totally for fun. And what’s more fun than finals season on “American Idol” and “The Voice”?

I’m often asked about (and asked to speak about) the predictive power of social media analysis, and I always tell people it’s more of an art than a science. Also, the predictive power of social media numbers varies according to what you’re trying to predict. Events that are rooted in popularity, though, correlate pretty well with numbers you see in social media, and that’s especially true where the events have a voting system that isn’t just one person, one vote (like political elections). Hence, televised singing contests where the audience votes.

We’re well into “Idol” right now — we’re down to the Top 3, one of whom gets booted this week, and next week is the finale — so I’ve decided to take a look instead at “The Voice,” which is several weeks away from its finale and still has, as of this writing, 16 contestants. Tonight, four will go home — one from each judge’s team. The way the process is supposed to work, the top two vote getters on each of the four teams advance, and then each judge gets to choose among his/her bottom two for who advances and who goes home. Therefore, audience votes get you in the top two, but then it’s up to the judges — not the best scenario for showcasing predictive power of social media.

Nevertheless, let’s look at the data we have. First, the contestants, their Twitter handles, their current follower count, and their Klout score:

Contestant Twitter handle Followers Klout
Danielle Bradbery @dbradbery 32352 66
Holly Tucker @hollytmusic 10232 65
Justin Rivers @justinrivers 58910 63
Swon Brothers @theswonbrothers 10644 63
Sasha Allen @sashaallenmusic 13735 68
Garrett Gardner @garrettgardner2 16893 63
Kris Thomas @kristhomasmusik 9721 65
Karina Iglesias @karinaiglesias_ 9024 64
Caroline Glaser @carolineglaser 40132 68
Judith Hill @judith_hill 36078 73
Sarah Simmons @sarahsimmusic 20826 66
Amber Carrington @ambercarrington 11274 63
Josiah Hawley @josiahhawley 29100 68
Michelle Chamuel @michellechamuel 14872 66
Vedo @vedothesinger 59217 72
Cathia @cathiasings 10527 66

Certain contestants “punch above their weight” when you compare their Klout score to their follower count; Klout’s metrics are proprietary but place a greater emphasis on engagement (e.g., replies, retweets, etc.) vs. pure potential audience size.

Just as a comparison point, we if we look at the three remaining contestants on “Idol,” we see the following:

Contestant Twitter handle Followers Klout
Angie Miller @angieai12 143664 79
Candice Glover @candiceai12 78983 78
Kree Harrison @kreeai12 74503 74

So even if “The Voice” is beating “Idol” in the ratings, the “Idol” contestants have a greater social media presence by several measures than do the “Voice” contestants. An interesting side note: all “Idol” finalists — including the ones that have long since gone home, have “verified” accounts on Twitter (meaning that Twitter considers them celebrities), whereas none of the “Voice” contestants do. This suggests to me that a deal was brokered between the “Idol” producers and Twitter.

Then we have actual buzz on social media. In my Attensity Media account, I set up a “Voice” topic and also created specific “entities” for all of the contestants that conflated their names, Twitter handles and hashtags so that I could get their counts in one place. I did set this up pretty late on Monday, after the show had aired in prime time, so the “Team Adam” and “Team Usher” contestants (who performed that evening) will have lower numbers than the “Team Blake” and “Team Shakira” contestants, who performed last night. Anyway, here’s what the live dashboard looks like:

The Voice May 6 1:49 PDT

So what do we have? We can compare two sets of numbers: general social media popularity and current week’s “buzz,” keeping in mind the grouping by judge. If we do that, we get:

Team Adam

Caroline Glaser 40132 68 2690
Judith Hill 36078 73 2500
Sarah Simmons 20826 66 1238
Amber Carrington 11274 63 1101

Team Blake

Justin Rivers 58910 63 4671
Danielle Bradbery 32352 66 10598
Swon Brothers 10644 63 4011
Holly Tucker 10232 65 5167

Team Shakira

Garrett Gardner 16893 63 5388
Sasha Allen 13735 68 5515
Kris Thomas 9721 65 4192
Karina Iglesias 9024 64 2439

Team Usher

Vedo 59217 72 1516
Josiah Hawley 29100 68 1784
Michelle Chamuel 14872 66 1728
Cathia 10527 66 1125

Again, the buzz numbers are artificially low for Team Adam and Team Usher contestants since I set up the topic late. There’s also the judges’ discretion in which of the bottom two vote-getters each judge decides to eliminate. That said, we’ll see tonight how predictive the social numbers are. Enjoy the show!

Another Race in Paradise

My wife’s a travel writer and covers Hawaii extensively, and one of the considerable perks I enjoy by virtue of being married to her is the ability to tag along on some of her work trips. And so it was that I found myself on Maui last weekend during the Maui Oceanfront Marathon festival of races. There were quite an array of races all happening on the same day: a marathon, a half marathon, a 15K, a 10K and a 5K. The half and 10K were on  an out-and-back course starting and finishing in Lahaina, and the rest were point-to-point affairs, requiring a shuttle bus to get to the start. All races finished in the same place in Lahaina town. We were staying up in Napili, right next to the Kapalua resort, which made my choice of the half work pretty well logistically.

Not that I had bothered to bone up too much on the logistics – this was strictly a “fun race,” an early season test of running fitness. I was hoping to go under 1:25, or just slightly faster than 6:30 pace. We’d had a nice dinner at Merriman’s Kapalua the night before, complete with wine pairings, which I heartily recommend — other than perhaps the night before a half marathon. 🙂 Woke up to some slight GI distress, which I won’t go into detail on, but I wasn’t feeling that race ready.

Got to the start line at 6:25 a.m. in plenty of time for a 6:45 a.m. start. However, it turns out that the start was at 6:30 a.m. (did I mention I hadn’t paid too much attention to the logistics?), so I jumped in near the front of the field and figured I’d do my warmup in the first mile. The horn sounded and we were off.

I was running pretty relaxed, and in the first mile I was probably in about 10th place overall. I knew that there was at least one other race going on at the same time — a 10K — but I wasn’t sure if there was a 5K as well. So you really couldn’t tell who was in which race. Plus it was pretty dark out at that hour — I ran with my sunglasses in my hand until there was enough daylight to put them on.

I started reeling in runners after mile 1, which I passed in a somewhat leisurely 6:35. The first female was my first passee, then I came up on a group of three guys running together. I went past them and surged as I did, just to discourage anyone from sitting on my wheel. One older guy in a “Yukon” singlet did sit on, then surged past me, which I thought was an interesting move, so I tucked in for a little while. Mile 2 was 6:19, so the surging had definitely picked the pace up. I was trying to stay relaxed, though — there was still a long way to go.

“Yukon”‘s breathing was pretty labored, and I could sense him slowing, so I surged past him again, this time for good. Next up ahead were three other runners, and I was starting to close in on them. I hit mile 3 in 6:27 (there was a bit of uphill in that mile), and all of a sudden, the three runners 20 yards a head of me turned at the 5K cone. I thought I was supposed to go on for another 3.5 miles to another turnaround for the half, but the road ahead of me looked closed — there were red cones lined up on the shoulder. So I second-guessed myself and thought that maybe it was a two-lap out-and-back course or something, so I turned back around to follow the others.

I had lost some ground to them during my hesitation, but started reeling them in much more quickly. Mile 4 came in 6:22, right at the point I passed a guy in a Laguna Niguel singlet; then all I could see were two guys together right up ahead of me. I went past them pretty quickly, and now there were only runners coming the other way on their way out. One woman high-fived me and said I was in the lead. That didn’t seem quite right, but I was just focused on staying relaxed and dealing with occasional rumblings from my gut — nothing severe, but I was a little worried about them in the second half of the race.

I hit mile 5 in 6:25, so I was still on goal pace, and behind me I could hear someone coming up on me. It was one of the last guys I had passed, and he looked as though he was making his finishing surge in the 10K. He pulled even with me, and I looked at him and gave him a “good job” nod before letting him go — I still had 7+ miles to go.

Or so I thought. As I came up to the start/finish line, it appeared that there was only a finish chute, not a place to turn around and go back out, and furthermore I was being announced as the 2nd-place finisher! The official time was 40:24 — not the 10K time I would have liked on my permanent record, but oh well.

At least I got to get first dibs on the free post-race massage. 🙂

Lesson for the day: if you can’t be bothered to read the race instructions closely, don’t get bummed out when things go awry. Besides, as one of my friends pointed out, “you’re still on Maui after all.”

It's all good

It’s all good

“Coach Ian”

I had a pretty good 2012. Actually, a really good 2012, at least from an athletic perspective:

  • Two Ironman finishes, including the infamous 2012 IM St. George
  • 3-minute Olympic-distance PR of 2:15:01 at Malibu
  • 22-minute Ironman PR of 10:04:24
  • Best age group placings ever at Ironman events: 10th at St. George, 7th at Hawaii 70.3, 3rd at Arizona
  • Qualified for 2013 Kona at Arizona
  • All at age 50 🙂

Ok, turning 50 definitely helped with the placings and the Kona slot, but the rest is pretty unusual, particularly because I’m not a newbie to the sport — I did my first tri in 1983 and have done more than 100, many of which you can’t even find on Athlinks because they predate the WWW (“that’s right, sonny, back in my day we got our race results on paper, mailed weeks or months after the event, and we liked it!”). So what made the difference?

Two things:

  1. Emphasis on the bike.
  2. Measuring almost everything.

Triathlon, especially an event as long as the Ironman, is all about the bike. Well, sort of. You have to get through the swim without losing a ton of time, and of course you have to close the deal with a good run, but like many triathletes I come from a running background. That has always been my strong suit. But the bike is where you can gain the most time and set yourself up for your best run. So I started last year by riding 5-6 days a week, mostly on my trainer. Using, in the beginning, two Spinervals Super 6 programs to establish my base.

Measurement and planning was all thanks to TrainingPeaks, whom I had the pleasure to represent as an ambassador athlete in 2012 and again this year. I have limited training time, like pretty much everyone else, so I have to make it count. Here’s a good picture of 2012 on my TrainingPeaks dashboard:

TP 2012In the pie charts on the upper right, you can see visually the emphasis on the bike, both in distance (which you would expect) and in duration (which you would also expect, though to a lesser degree). You can also see, in the widget on the bottom right, my two most important metrics: Intensity Factor (“IF”) and Training Stress Score (“TSS”), both of which were invented by the TrainingPeaks folks. What the blue values (TSS per week) show is how I built early on for St. George, then recovered, then built big time in the late summer/early fall for Arizona. All at a fairly steady average IF of above .75 and below .85, which for me at least is a sweet spot for Ironman training.

Along the way last year, I also guided the training of one of my teammates, the infamous Mikey, who shows up in many of my “Garage of Pain” and other key sessions. He had a similarly stellar year relative to normal, other than some bad luck at Arizona, but the point is the program worked — not just for me.

It was such a success, in fact, that we’re rolling it out to the entire Team Sheeper crew of athletes this coming year. We’ll be using TrainingPeaks for early season fitness and strength building, and then for specific programs aimed at goal half Ironman (Wildflower, Hawaii 70.3, Vineman 70.3) and Ironman (Coeur d’Alene, Lake Tahoe, Kona) events. I’m really excited to play a role in combining the team traditions of fun and hard training with a data-driven approach for managing training load within busy lives and professions. I’ve lived it, and continue to live it, and now I get to teach others.

So I guess I’m “Coach Ian.” All I need now are some stretchy coach’s shorts and a whistle.

If you live anywhere near Menlo Park, come and check it out! We don’t bite…much.

Auto-generated 2012 in review

Every social network seems to be offering a “year in review” feature. WordPress (who hosts this blog) is no different. Here’s their auto-generated “annual report”:

Here’s an excerpt:

600 people reached the top of Mt. Everest in 2012. This blog got about 2,800 views in 2012. If every person who reached the top of Mt. Everest viewed this blog, it would have taken 5 years to get that many views.

Click here to see the complete report.

The presentation is actually pretty slick. But the information in it is pretty mundane and easily derived — number of views, number of comments — what we in the biz call “social activity.” The one part that’s not obviously derived is the one about where people are coming from and which search terms got those people to the blog. These stats require knowledge about location of the viewer, which in the case of blogs come from a mapping of IP address to locations (there are entire companies built around this idea, Neustar being one via their acquisition of Quova), or about the referring URL for the viewer — part of what’s called “clickstream analysis” — in the case of search terms.

What’s missing for someone who really cares about social analytics is a number of other things:

  • How many times the blog post was linked to in Twitter, Facebook and other social networks. WordPress would need to have access to the full content stream from the other networks in order to know the real number. What they do know via clickstream analysis is when someone actually clicks on the link and goes to the page. Which of course is the most important thing — if you tweet a link and no one clicks on it, does it matter?
  • Even the most basic content analysis — what were the key themes discussed (you’d see a lot about “triathlon” in mine, for example, even in this post thanks to this parenthetical comment), what was the sentiment in the comments, tweets and Facebook posts that mentioned each blog entry, etc?

These two items are non-trivial to auto-generate. The former requires much more openness among the various social networks, which due to privacy concerns and policies, business model and “monetization” strategies and competition for users’ attention are becoming increasingly balkanized and locked down. The latter requires automated content analysis of the kind my company Attensity does, and believe me this stuff is hard to do accurately in a general-purpose way.

But perhaps the 2013 year in review will add a little more intelligence and start to move towards something that is actually interesting.

Rise of the Brand Ambassador

This might be my first personal blog that talks about work. Or sort of about work. It’s also about triathlon. But only sort of. (Reader rolls eyes and awaits another disjointed blog post. Or stops reading altogether.)

My work specialty is text analytics (a combination of computational linguistics and business intelligence), mostly applied to social media these days. What that means is that we at Attensity analyze the content of what people say on social media along with all of the other “social graph” data: e.g., how influential people are, how things get retweeted, liked, +1d, etc. The critical part is how to accurately map random human language into structures that correspond to meaning, so that they can be counted, tracked and trended in useful ways. Oh, and do that on many thousands of posts per second without falling behind.

What’s come out of that work, besides a lot of variety (I’ve worked with the Fortune 500, the intelligence community and major media and entertainment companies) is a rapid-fire introduction to the business side of social media. We’ve applied our technology to everything from following the U.S. presidential elections (starting with the GOP primaries) and Arab Spring to who’s getting voted off each week on American Idol or The Voice. There’s a lot of subtlety to what goes on; it’s not just about identifying positive and negative sentiment. Particularly important is the role of influence.

Influence is harder to measure than it might seem — it’s way more complicated than how many followers you have on Twitter or friends on Facebook. Entire companies (e.g., Klout) have been built around the attempt to quantify influence, but even their presumably sophisticated metrics don’t ring entirely true to many. What’s clear is that influence is topic specific — if you look at the most-mentioned celebrities on Twitter at any given moment, for example, you’ll almost always find Justin Bieber at the top. However, on election day, if you looked at election-specific tweets, as we did for Bloomberg, the top celebrity aside from the candidates was Jay Z. (You can watch the video to find out why.)

(The reader is wondering when we’re going to talk swim, bike and run. Patience!) Topic-specific influence has created novel new ways for companies to market their products and brands: among them, the brand ambassador. If you think about traditional advertising, celebrities are often used as brand ambassadors, but celebrities are expensive. The social brand ambassador, on the other hand, doesn’t need to be a celebrity per se; they just need to influence a sufficiently large network of people on a particular topic.

Which brings me around to triathlon. I’ve found myself, quite unexpectedly, in the position of having become a brand ambassador. Not once, but twice already, and I’m likely to pull the trigger on a third. Why is that? There are many faster guys out there than I, though I’m reasonably quick for a 50-year-old age grouper. I can think of a few reasons:

  • I’m part of a community. I am very active on my triathlon team, and I race within my local community in addition to bigger races elsewhere.
  • I’m active on social media, but not overactive. I try to be interesting and honest, without oversharing. Hopefully I succeed more than I fail.
  • I am a gearhead. I will try almost any new product if I think it will give me an edge. I would never endorse a product just because I got it for free or heavily discounted — my litmus test is would I use this if I had to pay full price for it? Actually, in the case of TrainingPeaks and many of Wattie Ink’s sponsors, I am and already was a customer and avid user.
  • I work with other athletes to help them improve. I am eager to share what I’ve learned — which tools to use, which training sessions are most effective for a particular end goal — and to see my advice through to implementation. I think I’m most proud of the level I got my “Garage of Pain” training buddy Mike to this past year, even compared to my own results.

What’s ironic for my day job is that — so I’m told, anyway — one of my company’s investors at one point laid a bunch of printouts of various of my Facebook posts down during a board meeting and opined that it seemed that all I did was train and race. (If that were true, btw, I should have much better results than I’ve had.) Notwithstanding potential jealousy (he’s…um…not exactly the fittest individual on the planet) and probable violation of European privacy laws (he’s not a Facebook friend, so had no right to access any of my posts), he was missing the point on one of the central themes of a company he’s invested a lot of money in: social influence.

Becoming an influencer isn’t hard: write about what you know, be passionate, interesting and real, and connect your social presence back to a community of people at least some of whom you know in real life. Oh, and occasionally kick some ass in a triathlon or two. 🙂

An interesting side note on the confluence of work and hobby: the evening before the Wattie Ink Elite Team selection was announced, I got the following DM on Twitter:

wattie dmWhat was funny about that was that I had been on set at The Voice a couple of nights before, thanks to our work in media. While I had made a couple of random references to it on Twitter, I had mostly posted about it to my Facebook friends. So anyway, Wattie did his homework.

Maybe I’ll get him to be my lead investor next time. 🙂

Un-freakin’-real: Ironman Arizona 2012

Not even sure where to begin, other than this was the most fantastic, and in many ways unexpected, Ironman race I’ve ever had: Sometimes numbers don’t tell the story, but in the case of my race, they do:

  • 22-minute Ironman PR, 18-minute bike PR
  • 3rd in M50-54, my highest Ironman age group placing ever (previous best was 10th)
  • 60th out of the water in the age group, 7th off the bike, 3rd at the finish
  • 3rd fastest bike and run splits in the age group
  • 3rd and final Kona slot in the age group 🙂

To say that I’m stoked is an understatement. This was a breakthrough race for me – all the training I did the past few years, especially the bike emphasis I had this entire year, paid off big time: I’m finally “one of those fast guys” in Ironman that I never really believed I would become.

Oh, and did I mention I qualified for Kona?

So here’s how it went:


My training was really solid once I recovered from Ironman St. George back in May. I got a course PR at the 70.3 Hawaii race and started adding long flat rides in the summer, starting with just over 90 miles and then going to the full 112. This was done with my crew: Mikey was a constant, and on most rides we were also joined by our teammates Eric and Derrick — the Team Sheeper IMAZ crew. Derrick, Mike and I also did a three-day Palo Alto-Santa Barbara tour along the coast, which was not only an incredible experience (picture-perfect weather), but showed me that I had both power and the endurance to hold it. My last 112-mile flat ride in the South Bay was at an average power of 205 watts, which still allowed me to run well afterwards. This was about 10 watts higher than I held in my last IMAZ in 2010, where I went 5:22 on the bike, so I felt as though I had a 5:10 in me. My race plan was to do about a 1:10 swim, 5:10 bike, and (and this was the big longshot) 3:25 run. With transitions included, this would put me in the 9:5x range. Factoring in a more likely 3:3x run, it was going to be close to make it under 10 hours, but anything under 10:26 would be a PR.

I felt really good on race week and was raring to go. The only mental demons I had to overcome were the weight of my own expectations — being fit is one thing, but executing on race day is another — and the nightmare scenarios going through my mind about the swim start. In my experience, IMAZ has been a rough swim, and this year wasn’t going to be helped by having almost 3000 athletes in the water at the same time. Still, race morning inevitably came, and Mikey and I headed into the water together to try and watch one another’s backs for as much of the swim as we could.


I lined up as far to the left as I could; I like having an escape route, if only for peace of mind. The paddleboarders were trying to get a bunch of us to move right, but there was no way that was going to happen — too crowded. So they let us be, and soon the gun sounded. I was a couple of rows back from the line, but got off surprisingly well in terms of very little physical contact. I lost sight of Mikey immediately, though — when you’re a middle-of-the-pack swimmer, you’re surrounded pretty quickly, so it’s every man for himself. My strategy was to keep to the left as much as the paddleboarders allowed, and that kept me in pretty clear water, though of course I didn’t have fast feet to swim behind either. But I will take that over constant pummeling any day — for me, the point of the swim is to get through it without expending a ton of energy. The only scrums came at the turn buoys, but people weren’t out to kill one another, so it was as good as it was going to be when you have tons of people cutting the tangents and trying to swim in the same space.

The way out to the buoys seemed to be faster than the return — I kept seeing the Mill Ave Bridge, but didn’t seem to be getting closer to it very quickly. I finally reached the stairs and took at peek at my watch. Ugh, five minutes slower than plan, but probably what I deserved. Grade: B-


IMAZ has wetsuit strippers, so I was down on the carpet quickly while the peeler did his work. I noticed my legs were a little crampy — I had been getting a few twinges towards the end of the swim. Wetsuit now off, I ran down to the bag area, got my bag and sat down on some grass outside the changing tent. Shoes on, sleeves on, helmet, sunglasses and race belt. Found my bike and headed out, and I got to the mount line right behind Mikey. Grade: B


I was very quickly in the groove on the bike. My power didn’t feel that great at first, but my speed was good and I was passing a lot of riders very quickly. I tried to get some fluids and a salt tablet in me once I got past the first series of turns and bunches of riders — the swim depletes you more than you think. I spent the better part of the first loop getting past lone fast swimmers and one or two packs — I’d call them “drafting packs,” but I don’t believe most people were intentionally drafting. It’s just hard to avoid bunching up when you have that many athletes on the course at the same time. The uphill on Beeline Highway also had a headwind on the first loop, so that made it a little easier to get past people; I just increased the power from around 210 watts to 240, and that put me past the packs. The ensuing downhill/tailwind did wonders for my average speed and let my legs relax a little — I was still pedaling but without as much tension in the legs. I noticed that my average speed was north of 22 mph, a first for me. It was my first inkling that this was going to be a faster ride than I had planned. I completed the first loop in 1:40:58, and headed back out.

This time, there were many fewer cyclists on the road, other than the ones I would catch later on in the loop that were a lap behind. I stopped to get my “special needs bag” around mile 60 — it had a bottle of my custom Infinit drink mix in it. On this loop, the wind had shifted to be somewhat of a headwind on the downhill back into town, but again my average speed hovered at 22.3 mph or so. There were a few riders I was trading the occasional place with, not in any organized fashion or anything. I remember being near a guy in Purplepatch kit near the end of the loop, which I reached in 1:41:43 (pretty much the same time as loop 1 if you count my stop for my special needs bag), and I rode away from him as I started the third and final loop.

Remember how you have a plan, and then stuff happens? At the next aid station, disaster struck — I missed several water bottles, and a well-meaning volunteer placed the water bottle at exactly the wrong place, and my thumb bent backwards very painfully. I thought for a few minutes I had broken it (I later realized it was just strained), but the immediate effect was a loss of concentration and of use of the hand. I couldn’t squeeze the bottle with it for awhile, so I switched to my left hand in order to get fluids in me. Near the top of the climb, I decided to get some calories in me, but the painful hand was making it difficult. At this point, the familiar figure of my teammate Mimi went by me, saying something that I couldn’t understand. I followed her into the turnaround, and on the ensuing downhill after the aid station, I re-passed her and turned on the gas. It’s funny how teammates have that effect on you more than random competitors you don’t know, but it was a needed kick in the pants. 🙂

I was setting all sorts of personal bests along the way — 100 miles in just under 4:31, 110 miles in under 5 hours — and I was still feeling great as I rolled into T2 in 5:04:27 with a last loop of 1:41:02 (I know the numbers don’t add up, but I must have started my bike computer a little after I got on the bike). Anyway, here’s a screenshot of the TrainingPeaks file:

Grade: A+


I executed a pretty good dismount thanks to my easy-in-easy-out Specialized bike shoes. My first barefoot strides off of the bike were gingerly, but I hurried to my bag and into the changing tent, dumped the contents, put socks and shoes on, and grabbed my visor and container of salt caplets. Volunteers applied sunscreen to my shoulders and neck, and I decided a quick visit to the port-a-potty was in order. I entered the run course the soonest I had ever done so: less than 6 hours, 30 minutes into the race. That meant if I could run a 3:30 flat or faster, I would go sub 10. Grade: A-


I felt awesome at the beginning of the run and went out in a 7:02 first mile. “Too fast,” I told myself, so I settled in to a 7:30 pace. Coming down a slight grade towards an aid station, my hamstring cramped, so I walked the aid station, got a salt caplet and some fluids in, and started running again. I was able to hold a nice pace for quite a while, seeing my support crew Greg and Alexa coming down off the Mill Ave Bridge after mile 3. The photo they took makes me look (a) fast and (b) as though there’s no one else around. It is true that the number of runners around me was the smallest I had ever experienced in a race; I guess that’s what a quick bike split will do for you.

I held around 7:30 pace for quite a while (I hit mile 8 in under 1:01) but gradually my pace slowed into the 8s. Hit the half marathon right at 1:41 and needed a 1:49 second half to just dip under 10 hours (if the math I was doing in my head was correct).

I couldn’t do it — I was running on the edge of cramping for quite a while, so couldn’t stride out like I wanted to. It was survival mode. I continued to take sports drink, water, sponges and Coke at pretty much every aid station, which kept me going. I knew, though, that I no longer had the leg speed to go under 10, but I knew that my time would be good if I could just hold it together.

I had no idea what place I was in or anything — in fact, I had no idea the entire race. I was just trying to execute my plan; the placing was up to the other competitors. 🙂 In the final mile, a guy came up on my left and asked how close we were to 10. I said it was close, but only if you have a lot left. He took off in what passed for a surge at that point (neither of us was moving that fast), and I looked at my watch and saw 9:57:xx. But there was still some ground to cover.

Finally I saw the blessed sight of the left hand turn towards the finish, which leads you out through what looks like a Hollywood backlot and onto a main road (where I spotted Greg and Alexa), then finally a left into the last 100 yards of finishing chute. I was absolutely ecstatic — I did some exaggerated arm movements as I hurried down the chute, high-fiving a few kids and putting an exclamation on a race that had gone almost perfectly. 10:04:24 was a new Ironman PR by over 22 minutes, and my run split of 3:35:42 was my 2nd-fastest Ironman marathon. Grade: A


After I got my medal and space blanket, I reunited with Greg and Alexa, who told me I had gotten 3rd in the age group. I was speechless. I had never made the podium before — frankly, it had never crossed my mind that that would be a by-product of that kind of finish time. I just never saw myself as “one of those podium guys” in an event as competitive as an Ironman.

Then people started asking me via text and Facebook about whether I got a Kona slot, and I thought I probably had but couldn’t confirm for sure. We did have to wait until the next morning to confirm it by going down to the registration area — that’s where I saw the magic list with numbers of slots per age group. Quick scan to M50-54: 3! Kona, baby!

I don’t know if I can ever duplicate this performance or experience. I’m certainly going to try, though. 🙂

No day like this ever happens on your own. There are so many to thank, but first and foremost my incredible support crew of Greg and Alexa, who got me through both St. George and Arizona this year. Also my “Garage of Pain” training buddy Mikey, who got into incredible shape this year and deserved a much better day in Tempe. To our long-ride cohorts Mimi, Derrick and Eric, I guess we know the route to Hollister better than anyone now. There are also some incredibly strong Team Sheeper teammates who inspire me to try to be as good as them (I’m not): Lennard, Jess, Vaagn, I’m coming for you. 🙂 Pierre and Wingman, bury the Lance hatchet and join me on a ride again — I miss you guys. Tim Sheeper, thank you for reminding me to be a warrior. TrainingPeaks, thanks for the software you make and for believing in me to represent you for another year. And finally, Jeanne, who puts up with this strange endurance sports lifestyle I have and is the one I’m most happy to share any meager success with.

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Shoreline XC 4M

I wasn’t planning to race today, but I got pressed into service yesterday afternoon by my San Francisco-based running team, who needed another runner in order to field a full masters team at today’s Shoreline cross country race. Too bad I found that out after yesterday’s short-but-hard bike/run combo, which included a spirited climb up Page Mill Rd, a trail run off the bike in Skyline Ridge, and then a speedy return via Skyline and Hwy 84, dodging Pumpkin Festival traffic on the twisty descent. All of this was followed by an Ironman-watching party at a friend’s, in which several glasses of anti-oxidant-rich fluid (i.e., a nice claret) were consumed.

Anyway, sometimes you gotta play hurt. I made my way down to the parking lot sandwiched between Shoreline Amphitheater and some Google buildings for a hilly, multi-loop 4-miler. It’s one of my favorites of the XC Grand Prix series, mostly because it’s not very XC like: packed dirt and gravel fireroads with some pavement thrown in. Forget the XC spikes. I felt none too spry during the warmup, met up with my teammates shortly before the start, and we were off.

XC races are notorious for starting hard off the line, and this one was no exception. However, since I have no top end any more, I held back and tried to negative-split the race. That meant I was near the back of the pack in the first 400m, but gradually started reeling geezers in. The first uphill came at about .5 miles, and was the first of three such loops. The good thing was that I thought the race was 5 miles (the old course I ran several years ago was), so I was pleasantly surprised when I realized the suffering would end sooner. Mile 1 was 6:20 – I used to run half marathons faster than that. 🙂 Oh well, kept the rhythm going and started picking off a few more guys, and then pretty much maintained position. Miles 2 and 3 were 6:18 and 6:16, so I was doing a decent job of running negative splits.

At least I’m not DFL








Down to the final downhill, and the two guys in front of me started their kick, and that’s when I realized how little speedwork I’ve been doing for the past few years of Ironman training – I had no answer. Final mile (or .98, according to my Garmin) was 6:02. So I just slotted under 25 minutes for the 4 miles and was the 5th runner (and scorer) for the team. Went for an easy hour cooldown run with some teammates and headed out for a nice brunch, just as the sun came out and we warmed up into the 70s.

Sauntering across the line









Life is good.

For my fellow data geeks, my TrainingPeaks file: