Wacky baseball night!

Baseball fans in the US are now well familiar with the epic collapse of the Boston Red Sox and the Atlanta Braves.  Last night was probably the most exciting night of baseball I have seen in at least ten years.  There are many, many places to read about what happened to the Red Sox and Braves.


I wish to point out something else that interested me last night.  There were four shutouts last night, three of which were complete-game shutouts by starting pitchers.  The most important of the four was Chris Carpenter's gem against the Houston Astros.  Carpenter gave up just two hits and one walk while striking out 11.  Also throwing a two-hit shutout was Miguel Batista of the New York Mets.  He blanked the Cincinnati Reds as the Mets finished the season at 77-85.


The one run the hapless Minnesota Twins scored for Carl Pavano was enough as Pavano shutout the Kansas City Royals while giving up five hits.  The win kept Minnesota's loss total for the season at 99.


Finally, the Seattle Mariners were shutout last night by two pitchers for the Oakland A's.  Gio Gonzalez gave up just two hits in eight innings; Andrew Bailey got the save after pitching a scoreless 9th inning.  It is a fitting end to the Mariners' season as they finished dead last in baseball in on-base percentage, slugging percentage, batting average, and runs scored.  The team's on-base percentage was 0.292 for the season.  Ouch.  Scoring just 556 runs in 162 games, the Mariners averaged about 3.43 runs per game.  The American League ERA average was 4.08 with the Angles leading the pack at 3.57.  Pitching against Seattle this past year meant that every team was better than the best pitching staff in the league!


There were 323 shutouts in major league baseball this past season; four of them took place last night.  There were 2429 total games played (the Dodgers and Nationals missed a game against each other), meaning shutouts happened in about 13.3% of the games.  There were four shutouts last night in the 15 games played, or about 26.7% of the games, which is double the seasonal average.  I know not to make much out of single points of data, but I did find it interesting that four shutouts took place on the last day of the regular season.

So long to the Eagles ...

Italy defeated the US in the 2011 Rugby World Cup, 27-10.  Italy got a bonus point, which sets the stage for a great match with Ireland on 2 October.  My initial thought was that Ireland would make it through, but there is still work to do against Italy.


Congratulations to Italy for a great win against the US.  My country's team is now out of the direct qualifying pool for the 2015 World Cup in England.  It will be interesting to see how rugby progresses in the US from here.

New Marathon World Record!

Congratulations to Patrick Makau Musyoki of Kenya for setting the new world record in the marathon at the Berlin Marathon on 25 September 2011.  The new record is 21 seconds better than the old, which was set almost three years ago by Haile Gebrselassie of Ethiopia.  Musyoki finished the 26-mile and 385-yard race in 2 hours 3 minutes and 38 seconds.


What was Musyoki's average speed?  Take the distance traveled (26 miles 385 yards or 42.195 km) over his record-breaking time and get 12.724 mph = 20.478 km/hr = 5.688 m/s.  Put another way, Musyoki averaged 4 minutes 42.927 seconds per mile.


I have never run a mile in under 5 minutes in my entire life.  Musyoki averaged better than 4.75 minutes per mile for more than 26 miles!


Note that 25 September 2011 is an historic day in Kenya.  Besides Musyoki's new marathon world record, Kenya lost one of its greatest citizens.  Wangari Maathai, who won the Nobel Peace Prize in 2004, died on the same day.  A phenomenal political and environmental activist, Maathai's Nobel Peace Prize marked the first time the award was given to an African woman.  Click here to learn more about Maathai's work.

Wow ... Australia!

It is not easy following the 2011 Rugby World Cup while living in the US.  Online updates were the best I could muster for my Eagles match against the Wallabies.  Though we competed well against Ireland, we got hammered by Australia earlier today, 65-7.  Congratulations to Adam Ashley-Cooper for scoring the fastest hat-trick ever in Rugby World Cup play.  Ashley-Cooper accomplished the feat in just seven minutes.  As obvious as it is to state, the US simply got clobbered by a much better team.


Australia and Ireland certainly look to advance out of Pool C, and that is probably not a shock to rugby fans.  I am happy, however, to learn something about rugby in the US while following this year's Rugby World Cup.  Even without the US in the knockout stage, I will keep following the other teams.  I just hope I can watch some of the games.


The sport is relatively new to me, which makes learning about it a lot of fun.  To nobody's surprise, there is a lot of interesting science in rugby.  I am still reading The Physics of Rugby by Trevor Lipscombe (click here to get the book), and I am enjoying it.  Though the physics is familiar to me, the rugby jargon and intricacies of the game are not.  My ignorance of the game is driving me to learn more about it, its rules, styles of play, tactical subtleties, and legendary players.  Rugby is definitely growing on me! 

A blocked punt leads to the Jets win over the Cowboys!

I analyzed Joe McKnight's great blocked punt from this past Sunday's Jets win over the Cowboys.  This was done at the request of YAHOO! SPORTS.  Click here for the link to the article by Kristian Dyer.

My introduction to rugby ...

Growing up in the US meant that my sports interests were dominated by baseball, basketball, and football, the "Big Three" American sports.  While in graduate school, I was introduced to other sports by friends and colleagues.  I saw my first soccer game when I was about 24 years old; I played cricket for the first time when I was about 25 years old.  As my research moved into sports physics, I became a lot more familiar with sports that are popular outside the US, like soccer and cycling.  Studying the aerodynamics of soccer balls and modeling the Tour de France have opened my eyes to wonders in sports I never knew as a child.


Today is a special day in the US.  We remember that terrible Tuesday morning ten years ago when we were so viciously attacked.  Thousands of innocent people lost their lives because a group of people had no respect for human life.  Feel free to read countless words elsewhere for analysis of the pernicious people who were responsible.  My blog concerns physics and sports, and sometimes a little more.  Hate and fear are borne out of ignorance.  Even in the sports world, ignorance of a given sport may lead one to dislike that sport at first sight.  I was that way with soccer.  After just a cursory peek at the game as a child, I thought it was boring, certainly not like the action in the "Big Three" American sports.  It wasn't until my mid thirties that I really watched soccer, and then grew to love The Beautiful Game.


While living in Sheffield, England during my 2008-09 sabbatical year, I had great fun watching "football" in pubs.  I also had a lot of fun watching a sport I knew very little about -- rugby.  On a trip to Ireland, my family saw the Irish national team play on television while we had a fantastic meal at the Brazen Head Hotel in east Dublin.  After seeing a few more rugby matches in English pubs, the game grew on me a little.  Nothing like removing ignorance of something to like it a little more, right?


Instead of watching American football on its opening Sunday today, I watched the NBC replay of the US vs Ireland match in the 2011 World Cup of Rugby.  Despite the fact that the US lost by the score of 22-10, I rather enjoyed the match.  My country's team is clearly not as good as the Irish team, but I admired the way we fought on defense.  The rainy weather and some sloppy Irish passes made me appreciate how much physics there is in rugby.  Reducing friction between the ball and a player's hands does not make for good passing!


My interest in the science of rugby has grown through knowing Trevor Lipscombe, my former book editor at The Johns Hopkins University Press.  I have just started reading Trevor's book, The Physics of Rugby, and it is a wonderful read.  Click here to get a copy from Amazon.  I highly recommend it!


Finally, I learned something else while watching the halftime show on NBC.  I did not know the name Mark Bingham.  Born just 106 days before I was, he played on championship rugby teams at UC Berkeley.  Mark Bingham was one of the heroes on United Airlines Flight 93, which went down ten years ago today.  I'm glad to have watched the rugby halftime show because I got to learn about Mark Bingham.  Click here for his Wikipedia page.  Click here for efforts made after Bingham's death to give people who are victims of prejudice an opportunity to shine on a rugby field.

He gave up FIVE HOMERS ... and got the WIN!

I saw an interesting box score last night.  Click here for the box score of the Rockies win over the Reds.  Thinking about the absurdity of the "win" and "loss" stats in baseball got me thinking once again about science and sports.


In physics, we concern ourselves with cause and effect.  We want to understand why a hit baseball eventually returns to Earth just as must as we want to understand why an electron moves around the nucleus of an atom.  Questions of why often involve a little philosophy because we use words like "gravity" and "electromagnetic force" to explain the baseball and the electron, respectively, even if we really don't understand what those interactions are.  Richard Feynman once noted that we use the concept of "energy" all the time, but we really don't understand what energy is.


Perhaps we in science do better with how something works.  We know how a baseball will move through the air because we have developed good models for gravity, air resistance, and the Magnus force that's responsible for a baseball curving.  If we tuck philosophy under the rug, we feel good about our ability to describe why a baseball does what it does.  We have a reasonable understanding of its motion (there are, however, still interesting questions to answer in the realm of baseball physics!).


Sabermetrics tries to understand the why in what happens in baseball.  What can batters do as causes that best lead to the effect of runs on the scoreboard?  What can pitchers do as causes that best lead to the effect of the opposing team not putting runs on the scoreboard?  At the end of a game, the winner is determined by who has scored the most runs.  How those runs were scored, be it by a bunch of home runs or via "small ball," is irrelevant.


Think about what a pitcher can control.  He can strike out or walk a batter essentially all on his own.  The manager or pitching coach might signal the catcher to signal the pitcher what pitch to throw and where to throw it, but the pitcher is the one who has to make the pitch.  A pitcher can also give up a home run.  The fielders can't do anything about a ball sailing into the stands.  The pitcher can also pick runners off base and throw certain pitches that try to "induce" things like ground balls that might lead to double plays.  Pitchers can also hit batters and throw wild pitches.  But, really, a strikeout, a walk, and a home run are where the pitcher is most on his own.  Everything else relies on the quality of the defense behind him, and subtle things like where managers have positioned the defense before a given player comes to bat also play a role.  The bottom line is that a pitcher cannot "win" or "lose" a game all by himself; it's a team effort.


I am certainly not the first person to point out the absurdity of the "win" and "loss" stats in baseball.  Though I've thought about it for more than two decades, this is the first time I've ever written about it in a public way.  Many others have written on this topic, and much better than I will today. See, for example, what the great Joe Posnanski recently wrote by clicking here.  Wins and loses might be fun stats and they have connections to baseball's storied past, but they do not say much about the cause and effect of what pitchers can do to prevent runs from being scored.  As others have written, the "win" is not completely useless as a stat, especially over the length of a player's career.  A pitcher who wins 300 games is a good (or great) pitcher, but the "win" stat doesn't tell the best story.  Over the course of a long career, it might reveal some averaging over "tough luck" losses (say, 2-1) and "lucky" wins (say, 10-9).  In the end, though, the career win total reflects how many games a pitcher pitched, how deep into games the pitcher was able to go (five innings needed for a starting pitcher to get a win), and how successful the pitcher's teams were.  Greatness can be hidden from those who focus too hard on wins.  Just click here to read what Rich Lederer has written since 2003 about the insanely long wait Bert Blyleven endured before getting the Hall of Fame call this past January.


Okay, back to last night's Rockies win over the Reds.  Alex White got the "win" for the Rockies, despite giving up eight hits, seven runs (six of them "earned"), a walk, and FIVE HOME RUNS.  He struck out just one batter.  He got the "win" because (1) he pitched five innings and (2) the score was 8-7 Rockies after the fifth inning ended, and the Rockies never gave up the lead.  As far as preventing runs goes, Alex White had an AWFUL game.


Who got the "loss" in last night's game?  Was it Bronson Arroyo, who started for the Reds?  He pitched just ONE inning and gave up seven hits, six runs (all eared), and struck out one batter.  Giving up five home runs in five innings is bad, but Bronson Arroyo gave up THREE home runs and was the pitcher of record on just three outs.  At least Alex White was the pitcher of record on 15 outs as he gave up seven runs.  Arroyo did not, however, get the "loss" in last night's game.  That went to Matt Maloney who pitched two innings and gave up two runs (one earned).  Matt Maloney was unlucky enough to have pitched the fourth and fifth innings, meaning he was the "pitcher of record" when Colorado took the lead for good after five innings.


So, does Matt Maloney feel like the "loser" in last night's game?  Does Alex White feel like the "winner" in last night's game?  Bronson Arroyo pitched worse than anyone in that game, but he got the "no decision" because his offense kept his team in the game for the first five innings.  Alex White was terrible for five innings, but his teammates scored enough runs to give him the "win."  Of course, four Rockies pitchers came in after Alex White and pitched four shutout innings.  Three of them settled for the wonderful "hold" stat.  Bronson Arroyo was so bad that he could not contribute to more than three outs, but Matt Maloney pitched the wrong two innings and wound up with the "loss."  At least he helped get six outs.  Sam LeCure helped on just three outs while giving up two runs, and Aroldis Chapman gave up two runs and wasn't a part of a single out (he walked two batters and threw a wild pitch)!


Last night's game is certainly not an anomaly, and I'm not just cherry-picking a strange game to make the argument that a pitcher's "win" doesn't tell us much.  Scan box scores every day and see if the "win" and the "loss" tell you much.  C.C. Sabathia, for example, is a good pitcher, but he accumulates wins better than some pitchers because his team scores a lot of runs.  Of his 19 wins, I count six games in which he gave up four or more runs.  C.C. Sabathia is having a great year because he is pitching a lot of innings, has a great strikeout-to-walk ratio (216 K to 55 BB), doesn't give up the long ball (just 15 this year), and has a stellar 150 ERA+.  The "win" total is high because C.C. Sabathia is not only a good pitcher, his team scores runs for him.  Per nine innings, C.C. Sabathia gets 7.06 runs from his teammates, good enough for 13th in the American League.  Don't fault C.C. Sabathia for his good run support, find his greatness in other, more meaningful, pitching stats.


Regarding last night's game in Colorado, I prefer to think that the Rockies got the "win" and the Reds got the "loss."  The Rockies did, after all, score more runs than the Reds before their allotment of outs was used.

Interview on iTunes

On 28 July 2011, I was interviewed by Bruce Berglund of New Books in Sports, which is part of the New Books Network.  A brief story behind the interview may be found here.  The 62-minute interview became available at the New Books in Sports website on 24 August 2011.  It is also available on iTunes here.


The interview was a lot of fun!  Bruce and I discussed portions of my book, including Flutie's famous Hail Mary pass, laterals in American Football, soccer aerodynamics associated with Beckham's free kicks, Beamon's famous Olympic long jump, modeling the Tour de France, and what happens when a diver like Louganis enters the water (see my book cover).  We also talked about topics outside my book, like baseball flight physics and the controversies surrounding the Jabulani ball used in the 2010 World Cup.  I had a little time to talk about the 2011 Tour de France.


We closed the interview with discussion about my current work, namely my investigations into boundary-layer separation on a soccer ball.  As I mentioned in the interview, if there are young people out there wishing to do research in sports physics, think about studying in the physics department at Lynchburg College.

EARTHQUAKE!!!

I just experienced the second earthquake of my life.  This one was bigger than the one I felt about seven years ago.  Click here for a link to the United States Geological Survey data of the Virginia earthquake of 23 August 2011.  The earthquake's magnitude was 5.9.  Just like the logarithmic scale I mentioned for sound loudness in my last post, earthquake magnitudes are also on a logarithmic scale.

JOURNEY!!!

I needed a break after the Tour de France ended.  My family took a fortnight-long holiday in the first half of August; I only got back to work last Thursday (18 August).  This is my first post since returning from holiday, and there will be very little physics in this one.


On 5 September I will turn 41.  My wonderful wife, Susan, treated me to an early birthday present yesterday (20 August).  She took me to my very first rock concert!  To top it all off, I got to see my favorite band, Journey.  We saw Night Ranger and Foreigner open for Journey at the Time Warner Cable Music Pavilion at Walnut Creek in Raleigh, North Carolina.  It was one of the best nights of my life!  We had great seats -- eighth row in the second section of reserved seating.


Night Ranger played a short set, including, of course, Sister Christian.  Foreigner was fantastic!  They played a bunch of classic hits.  By the time Journey got on the stage, I was acclimated to my first rock concert.  The noise was deafening at times.  Loudness levels were certainly above 100 dB.  Recall that loudness is measured on a logarithmic scale.  Pain at all frequencies occurs around 130 dB.  I wish I had a sound meter with me last night, but I would have been kicked out for being too nerdy!  Sound levels surely approached pain threshold a few times at our concert.  I now have a first-hand feeling for why so many rock musicians suffer hearing damage.


When asked about physics and sports, I always tell people to enjoy the sporting moment first, and then think about the physics later (even if just a minute later).  Physics is meant to enhance our enjoyment of the natural world; it is not meant to displace one's emotional experience of life.  I thought about sound levels only after our concert was over.  During the concert, I was mesmerized watching and listening to Journey play.  Seeing and hearing Neal Schon play a guitar in front me is something I will never forget.  At 57 the man's axe-work is still top notch.


After seeing scores of classical music concerts, I have now seen a rock concert.  So what if I did not see my first rock concert until I was nearly 41?!?  Better late than never, right?


I plan to add more sports physics posts in the near future.  For now, my ears need a little rest.  One piece of advice I can offer:  SEE JOURNEY LIVE IN CONCERT!!!

2011 Tour de France Summary

The table below summarizes the quality of my predictions in this year's Tour de France.

Stage	Actual	Predicted	Difference	% Diff.
1	4h 41' 31"	4h 40' 01"	-01' 30"	-0.53
2	0h 25' 16"	0h 26' 35"	01' 19"	5.21
3	4h 40' 21"	4h 44' 55"	04' 34"	1.63
4	4h 11' 39"	4h 09' 29"	-02' 10"	-0.86
5	3h 38' 32"	3h 52' 05"	13' 33"	6.20
6	5h 13' 37"	5h 20' 13"	06' 36"	2.10
7	5h 38' 53"	5h 11' 43"	-27' 10"	-8.02
8	4h 36' 46"	4h 49' 26"	12' 40"	4.58
9	5h 27' 09"	5h 19' 43"	-07' 26"	-2.27
10	3h 31' 21"	3h 41' 08"	09' 47"	4.63
11	3h 46' 07"	3h 46' 05"	-00' 02"	-0.01
12	6h 01' 15"	5h 59' 26"	-01' 49"	-0.50
13	3h 47' 36"	3h 46' 52"	-00' 44"	-0.32
14	5h 13' 25"	5h 02' 45"	-10' 40"	-3.40
15	4h 20' 24"	4h 36' 38"	16' 14"	6.23
16	3h 31' 38"	4h 05' 59"	34' 21"	16.23
17	4h 18' 50"	4h 32' 07"	13' 17"	5.13
18	6h 07' 56"	5h 51' 23"	-16' 33"	-4.50
19	3h 13' 25"	2h 57' 54"	-15' 31"	-8.02
20	0h 55' 33"	0h 51' 06"	-04' 27"	-8.01
21	2h 27' 02"	2h 27' 40"	00' 38"	0.43
TOTAL	85h 48' 16"	86h 13' 13"	24' 57"	0.48

I predicted six stages (I mistakenly noted five yesterday) to better than 1%.  Only the enigmatic Stage 16 came in over 8% (rounded) off.  I am quite pleased by my predictions!

Note the last row in the above column gives the sum of the stage-winning times.  My model cyclist completed the Tour de France in a time 0.48% slower than the sum of all the stage-winning times.  Note that that time is not the total time posted by this year's winner, Cadel Evans.  His winning time was 86h 12' 22".  Though that time is just a mere 51 seconds off from the sum of my stage-winning times, my goal at the outset was not to predict the overall time for any one cyclist, but the sum of the stage-winning times.

The overall error of 0.48% is a bit misleading because it comes from a lot of cancellation.  For example, I was 27' 10" fast on Stage 5 and 34' 21" slow on Stage 16.  Those "fast" and "slow" times tend to cancel at the end.  If I add error in quadrature, I wind up with a 1.16% error, which is still not bad!

Modeling the Tour de France is a lot of fun for me, and it enhances my pleasure in following the race.  My stats page tells me that people from 21 different countries checked out this blog during the course of the race.  I am humbled and flattered by the level of interest in my blog.  Please contact me with any questions you may have.  If you want more details about Tour de France modeling, check out Chapter 4 in my book, Gold Medal Physics:  The Science of Sports.

I plan to add more sports science posts in the future.  Feel free to send me suggestions of sporting events and/or athletic feats that might benefit from the eye of a physicist.

Nearly perfect on Stage 21!

Here is the result from the final stage of the 2011 Tour de France:

• Stage 21:  2h 27' 02" (actual), 2h 27' 40" (prediction), 38" slow (0.43% error)

Given how the last stage is usually run, I am quite happy with my prediction!  I asked yesterday if Mark Cavendish could win his third straight final stage -- and he did!  Here is his average speed:

• Stage 21:  10.77 m/s (24.1 mph)

Congratulations to Cadel Evans for becoming Australia's first Tour de France winner!

Tomorrow I will post a summary of my predictions for this year's Tour de France.  Five stage predictions came in under 1% off.  Just one stage came in over 8%, and that was my dreadful Stage 16 (still an enigma for me!).  I am happy with how my model did!

A bit fast on Stage 20 ...

Here is the result from today's individual time trial:

• Stage 20:  55' 33" (actual), 51' 06" (prediction), -04' 27" fast (-8.01% error)

Just like with Stage 19, I was 8% too fast on the penultimate stage.  I guess Stage 16 convinced me that I needed a little more power -- a little too much it turned out.  Here is what Tony Martin was able to average in his win today:

• Stage 20:  12.75 m/s (28.5 mph)

To answer my question from yesterday, "Yes, the Schleck brothers can be stopped!"  Let's hear it for Australia!!!  Cadel Evans came from down under to sit atop the cycling world.  Australia will surely celebrate its first Tour de France winner after tomorrow's ride into Paris.

The last stage is always the hardest to model.  I can take the terrain data and predict what a top cyclist would do.  The last stage, however, is usually not as hotly contested as previous stages.  This year's final stage is rather short at just 95 km (59 miles).  Here is my prediction:

• Stage 21:  2h 27' 40" (prediction)

I have dialed the power input back quite a bit.  Will Mark Cavendish win his third straight Tour de France final stage?  Were the final stage fought as hard as possible, I'd probably lop 15 to 20 minutes off the above time.  But, I am trying to predict what will happen, so I am going with the slower time.

A bit fast on Stage 19 ...

I thought someone could finish Stage 19 in just under three hours.  I was wrong about that!  Here is the result from Stage 19:

• Stage 19:  3h 13' 25" (actual), 2h 57' 54" (prediction), -15' 31" fast (-8.02% error)

Pierre Rolland had a great climb at the end to overtake Alberto Contador.  Here is Rolland's average speed:

• Stage 19:  9.44 m/s (21.1 mph)

I'm always glad to be under 10% on my predictions.  Even though I was 8% off today, I thought the stage could have been done a little faster.  Here is my prediction for tomorrow's individual time trial:

• Stage 20:  51' 06" (prediction)

Can the Schleck brothers be stopped?

Back under 5% for Stage 18!

Here is the Stage 18 result:

• Stage 18:  6h 07' 56" (actual), 5h 51' 23" (prediction), -16' 33" slow (-4.50% error)

I am happy to be under 5% again!  Did the poor weather slow riders down a little?  If so, I feel even better about my prediction because my model did not include adverse weather for Stage 18.  Then again, it rained on Stage 16, and that one remains a mystery to me.

The Schleck brothers got it done today.  Here is Andy Schleck's average speed:

• Stage 18:  9.08 m/s (20.3 mph)

Thomas Voeckler was able to hold on to the yellow jersey.  Tomorrow's stage, which is the final mountain stage, might just decide this year's winner.  Here is my prediction:

• Stage 19:  2h 57' 54" (prediction)

Stage 19 should be incredible!  Riders climb to an elevation of 2.556 km (1.588 miles) as they reach Col du Galibier in the French Alps at the 48.5-km (30.1-mile) mark.  The next 46 km (28.6 miles) will then make for a great downhill.  The final 15 km (9.32 miles) will have riders climbing a 7.9% grade to get to the famous ski village at L'Alpe d'Huez.  In 2004, Lance Armstrong dominated that climb in that year's Stage 16, which was an individual time trial.  Armstrong beat the second-place finisher, Jan Ullrich, by just over a minute.  That stage sticks out in my mind because I cover it in detail in Chapter 4 of my book.

If weather does not slow riders down, I hope to see the winner's time sneak under three hours.

Much better on Stage 17!

Here is the Stage 17 result:

• Stage 17:  4h 18' 50" (actual), 4h 32' 07" (prediction), 13' 17" slow (5.13% error)

As with Stage 16, I was slow on this stage.  I am obviously much happier with a 5% error than yesterday's 16% error!  Stage 16 is still a mystery to me.  That stage was mostly uphill in the rain, and the winner's average speed beat every other stage winner's average speed (except that in the team time trial of Stage 2).

The Norwegian Edvald Boasson Hagen had the following average speed today:

• Stage 17:  11.53 m/s (25.6 mph)

Despite the near 50-km downhill near the end of today's stage, Hagen's average speed was nearly 10% less than Hushovd's Stage 16 average speed.  Again, what happened on Stage 16?!?  I welcome your thoughts in the comments.

Stage 18 has three monster climbs and two great downhill segments.  By the time riders reach the end at Galibier / Serre-Chevalier, they will have gained 2.29 km (1.42 miles) of elevation from their starting point.  Here is my Stage 18 prediction:

• Stage 18:  5h 51' 23" (prediction)

Can Thomas Voeckler hold the yellow jersey after tomorrow?  It should be a wonderful climb to the finish line!

Worst prediction ...

This is, by far, my worst prediction of the 2011 Tour de France.  Here is the result from Stage 16:

• Stage 16:  3h 31' 38" (actual), 4h 05' 59" (prediction), 34' 21" slow (16.23% error)

I am stunned by how fast this stage turned out to be.  Here is Thor Hushovd's average speed:

• Stage 16:  12.80 m/s (28.6 mph)

Except for the team time trial in Stage 2, the above average speed is the largest so far.  I never thought that a stage that is almost entirely uphill could have such a large average speed.  The God of Thunder certainly shocked me today!  Was there a massive tailwind today???

Here is my prediction for Stage 17:

• Stage 17:  4h 32' 07" (prediction)

I am shocked that today's stage was well under four hours.  Stage 14 was won in over five hours.  Tomorrow's Stage 17 does not have as many brutal climbs as were found in Stage 14.  Stage 17 should end with a great downhill sprint into the Italian city of Pinerolo.

Stage 16 prediction ...

Here is my prediction for Stage 16:

• Stage 16:  4h 05' 59" (prediction)

Can someone finish Stage 16 in under four hours?  We shall see!  It will take a rider with a lot of power input because the majority of the stage is uphill.  The stage should end with a great downhill sprint into Gap.

Congratulations to Japan

It was agonizing seeing the US women lose today, but I am happy for the Japanese team.  They played with a great deal of heart.  To keep coming back the way they did, Japan will take home a well-earned trophy.


For me, I loved being able to share the women's World Cup with my two young daughters.  They now know names like Alex Morgan, Abby Wambach, and Megan Rapinoe.  Each time Rapinoe had the ball, my girls exclaimed, "Rapinoe has it!  Rapinoe has it!"  After the game was over, my two girls went outside and played soccer.  I thank my national team for inspiring my girls to put boot to ball and experience a little of the beautiful game.

A bit slow on Stage 15 ...

Here is the Stage 15 result:

• Stage 15:  4h 20' 24" (actual), 4h 36' 38" (prediction), 16' 14" slow (6.23% error)

This is the one stage I'm kicking myself over!  After three grueling mountain stages, I knocked my code's biker power input down just a little for Stage 15.  Had I not done that, my error would have been cut in half.  The fact that I was wrong to do that actually inspires me because that means the athletes at the Tour de France are even better than I first thought.  Here is what Mark Cavendish was able to average in today's impressive win:

• Stage 15:  12.32 m/s (27.6 mph)

This is almost exactly the same speed Cavendish averaged during his win in Stage 11.  Certainly impressive!

Monday is a rest day.  I plan to publish my Stage 16 prediction on Monday.  I am hoping to get one or two more profile points.  Right now, I need to get a few other things done so that I can watch the women's World Cup final.  Go US!

A tad more realistic on Stage 14 ...

When I started modeling the Tour de France in 2003, I thought predicting a stage win to better than 10% would be pretty good.  Anytime I got a stage better than 5%, I thought I had done a great job.  The last three stages of this year's race were a little surreal.  I just can't predict every stage to better than 1%!  Stage 14 brought me back to reality a little, but I am still pleased with my prediction.  Here is the result:

• Stage 14:  5h 13' 25" (actual), 5h 02' 45" (prediction), -10' 40" fast (-3.40% error)

Though thrilled with just a 3.4% error, I think my model was a tad fast because I did not add a "Gee, I'm tired on this third mountain stage in a row!" line to my code.

Here is what Jelle Vanendert was able to average for Stage 14:

• Stage 14:  8.96 m/s (20.0 mph)

Stage 15 will give the riders a little relief because it is mostly flat.  Monday is a rest day.  Here is my Stage 15 prediction:

• Stage 15:  4h 36' 38" (prediction)

Will riders be tired on Sunday?  Will the ride into Montpellier be a relief for rider's looking forward to Monday's rest?  I can't wait to see what happens!

After the Tour de France, I will be watching our US women's soccer team take on Japan in the World Cup final.

Just 44 seconds off Stage 13!

Here is the result from Stage 13:

• Stage 13:  3h 47' 36" (actual), 3h 46' 52" (prediction), -44" fast (-0.32% error)

I'm once again shocked how well I predicted a mountain stage.  Thor Hushovd had a great ride.  Here is what The God of Thunder was able to average:

• Stage 13:  11.17 m/s (25.0 mph)

Stage 14 looks to be a brutal ride.  There will be, however, some amazing vistas along the way.  Here is what I predict for Stage 14:

• Stage 14:  5h 02' 45" (prediction)

After such a grueling couple of stages, can someone finish Stage 14 in under five hours?  I'm sure the riders will be thrilled to reach the Pyrenees ski resort of Plateau de Beille at the end of the stage.  They will then be at an elevation of 1.78 km (1.1 miles).

Nearly nailed Stage 12!

Here is the Stage 12 result:

• Stage 12:  6h 01' 15" (actual), 5h 59' 26" (predicted), -01' 49" fast (-0.50% error)

Wow, I'm really happy to hit the first major mountain stage to within half a percent error!  I thought the winner might be just a tad under six hours, but it turned out to be just a tad over.  Here is Samuel Sánchez's average speed:

• Stage 12:  9.73 m/s (21.8 mph)

Stage 13 has a great climb up to Col d'Aubisque.  Here is my prediction:

• Stage 13:  3h 46' 52" (prediction)

After a monster climb to the 110-km mark, it'll be a great downhill to the end at Lourdes!

On to the final!

Our US women played the beautiful game beautifully!  Tension rose after the French equalizer early in the second half.  Abby Wambach's header after Lauren Cheney's perfect corner kick was magical, as was the patience Alex Morgan displayed on her goal.


Kudos to the French women's team; they played very well.  They also displayed a lot more class than what their male counterparts displayed at last year's World Cup.


The best moment for me?  Once the game ended, my two young daughters were screaming, "We won!  We won!"

Missed Stage 11 by TWO SECONDS!!!

This is by far my best stage prediction of this year's Tour de France!  Here are today's results:

• Stage 11:  3h 46' 07" (actual), 3h 46' 05" (prediction), -02" fast (-0.01% error)

Here is Mark Cavendish's average speed for today's win:

• Stage 11:  12.35 m/s (27.6 mph)

Stage 12 is a great mountain stage, which finishes in Luz Ardiden.  There are three big climbs in the stage.  Ben Hannas sent me a couple of extra data points so that I could adequately model the motion through the valleys.  The net elevation increase from start to finish will be 1.55 km (0.96 mile).  Here is my prediction:

• Stage 12:  5h 59' 26" (prediction)

I am hoping that the winner can complete the stage in just under six hours.

Okay, it's time to get home to watch the US women take on France in the World Cup semifinals.

Women's soccer and soccer physics ...

We in the US are rooting for our women's soccer team to beat France in tomorrow's semifinal World Cup match.  People are still talking about the amazing comeback win over Brazil.  For those of you interested in soccer (or football, depending on where you are from), I wrote an invited article for the July 2010 issue of Physics Today that coincided with the men's World Cup.  The article is short and intended for a general science audience.  Click here for a PDF version of the article.


If you are interested in a longer description of soccer physics, but still at a general science level, Chapter 7 of my book is devoted to soccer kicks.  I model free kicks and corner kicks.


For those interested in my technical research papers on soccer physics, click here to get to my webpage.  You will find paper links at the bottom of the page.

Stage 11 prediction ...

Here is my prediction for Stage 11:

• Stage 11:  3h 46' 05" (prediction)

Ben Hannas once again provided me with some extra data points.

Still better than 5%!

After a great sprint to end today's stage, here are the Stage 10 results:

• Stage 10:  3h 31' 21" (actual), 3h 41' 08" (prediction), 9' 47" slow (4.63% error)

It turned out the extra seven data points took me a little further from the result.  The model with fewer data points may have averaged a little better, but I stand by the more detailed stage model.  Riders were a little faster today than I thought they would be!  Yesterday's rest must have helped.  I'm still thrilled to have predicted the time to better than 5%.

Here is the average speed for André Greipel's ride today:

• Stage 10:  12.46 m/s (27.9 mph).

I'm not surprised being a little slow on what turned out to be the race's second-fastest stage (excluding Stage 2's team time trial).

My prediction for Stage 11 is coming soon.

Day of rest ...

On 8 July, I gave the average speeds of the first seven stage winners.  Here are the average speeds of the last two stage winners:

• Stage 8:  11.38 m/s (25.5 mph)
• Stage 9:  10.60 m/s (23.7 mph)

There is no surprise that Stage 9 is the slowest so far, notwithstanding the crashes.  The climbs in the middle of Stage 9 hit some high elevations.  Riders better get plenty of rest today.  After a couple of modest stages, the great Pyrenees climbs await!

I'm anxious to see how my Stage 10 prediction does tomorrow.  I'm also anxious to know who is reading this blog.  Feel free to add a comment and let me know what you think of the Tour de France, scientific modeling, and anything else on your mind.  Let me know your country of origin, too.

Extra valley points did the trick on Stage 9!

Here are the results for Stage 9:

• Stage 9:  5h 27' 09" (actual), 5h 19' 43" (prediction), -7' 26" fast (-2.27% error)

The four extra valley data points did the job on Stage 9!  I'm happy with a 2.27% error.

For Stage 10, Ben Hannas sent me seven extra data points.  If you examine the stage profile online, you will see a few missing peaks and valleys.  With the added data, here is my prediction for Stage 10:

• Stage 10:  3h 41' 08" (prediction)

Without those seven extra points, my prediction would have been 3h 35' 57".  By adding in more detail, specifically more peaks and valleys, the predicted time goes up a little.