Calories in, calories out

Introduction

How do we lose (or gain) weight?  Is it really as simple as “calories in, calories out” (i.e., eat less than you burn), or is what you eat more important than how much?  Is “3500 calories equals one pound” a useful rule of thumb, or just a myth?  I don’t think I would normally find these to be terribly interesting questions, except for the fact that there seems to be a lot of conflicting, confusing, and at times downright misleading information out there.  That can be frustrating, but I suppose it’s not surprising, since there is money to be made in weight loss programs– whether they are effective or not– particularly here in the United States.

Following is a description of my attempt to answer some of these questions, using a relatively simple mathematical model, in an experiment involving daily measurement of weight, caloric intake, and exercise over 75 days.  The results suggest that you can not only measure, but predict future weight loss– or gain– with surprising accuracy.  But they also raise some interesting open questions about how all this relates to the effectiveness of some currently popular diet programs.

The model and the experiment

Here was my basic idea: given just my measured starting weight w_0, and a sequence (c_n) of measurements of subsequent daily caloric intake, how accurately could I estimate my resulting final weight, weeks or even months later?

More precisely, consider the sequence (\hat{w}_n) of predicted daily weights given by the following recurrence relation:

\hat{w}_0 = w_0

\hat{w}_{n+1} = \hat{w}_n + \frac{c_n - \alpha \hat{w}_n - 0.63 \hat{w}_n d_n}{3500}

Intuitively, my weight tomorrow morning \hat{w}_{n+1} should be my weight this morning \hat{w}_n, plus the effect of my net intake of calories that day, assuming 3500 calories per pound.  Net calorie intake is modeled with three components:

  • c_n is the number of calories consumed.
  • -\alpha\hat{w}_n is the number of calories burned due to normal daily activity.  Note that this is a function of current weight, with typical values for \alpha of 12 to 13 calories per pound for men, or 10 to 11 for women; I used 12.5 (more on this later).
  • -0.63 \hat{w}_n d_n is the number of additional calories burned while running (my favorite form of exercise), where d_n is the number of miles run that day.  Note that we don’t really have to account for exercise separately like this; especially if duration and intensity don’t change much over time, we could skip this term altogether and just roll up all daily activity into the (larger) value for \alpha.

(Aside: I am intentionally sticking with U. S. customary units of pounds, miles, etc., to be consistent with much of the related literature.)

So, at the start of my experiment, my initial weight was w_0=251.8 pounds (for reference, I am a little over 6’4″ tall, 40-ish years old).  Over each of the next 75 days, I recorded:

  • My actual weight w_n, first thing in the morning after rolling out of bed, using a digital scale with a display resolution of 0.1 pound.
  • My total calories consumed for the day c_n.
  • My running mileage for the day d_n.

Plugging in c_n and d_n to the recurrence relation above, I computed the sequence of predicted weights (\hat{w}_n), and compared with the sequence of my actual weights (w_n).

Results

The following figure shows the resulting comparison of predicted weight \hat{w}_n (in blue) with measured actual weight w_n (in red).  See the appendix at the end of this post for all of the raw data.

Predicted and actual weight over 75 days.

Predicted and actual weight over 75 days.

I was surprised at just how well this worked.  Two and a half months and nearly 30 pounds later, the final predicted weight differed from the actual weight by less than a pound!

There are a couple of useful observations at this point.  First, the “3500 calories per pound” rule of thumb is perfectly valid… as long as it is applied correctly.  Zoë Harcombe, a “qualified nutritionist,” does a great job of demonstrating how to apply it incorrectly:

“Every person who didn’t have that [55-calorie] biscuit every day should have lost 141 pounds over the past 25 years.”

This seems to be a common argument– professor of exercise science Gregory Hand makes a similar but slightly more vivid claim using the same reasoning about a hypothetical dieter, that “if she will lose 1 lb for every 3,500 calorie deficit [my emphasis], our individual will completely disappear from the face of the earth in 300 days.”

The problem in both cases is the incorrect assumption that an initial calorie deficit, due to skipping a biscuit, for example, persists as the same deficit over time, causing a linear reduction in weight.  But that’s not how it works: as weight decreases, calorie expenditure also decreases, so that an initial reduced diet, continued over time, causes an asymptotic reduction in weight.  (In the context of the recurrence relation above, Harcombe and Hand’s calculation effectively replaces the varying \alpha \hat{w}_n in the numerator with the constant \alpha w_0.)

Estimating \alpha

The second– and, I think, most important– observation is that I arguably “got lucky” with my initial choice of \alpha=12.5 calories burned per pound of body weight.  If I had instead chosen 12, or 13, the resulting predictions would not agree nearly as well.  And your value of \alpha is likely not 12.5, but something different.  This seems to be one of the stronger common arguments against calorie-counting: even if you go to the trouble of religiously measuring calories in, you can never know calories out exactly, so why bother?

The Harris-Benedict equation is often used in an attempt to remedy this, by incorporating not only weight, but also height, age, gender, and activity level into a more complex calculation to estimate total daily calorie expenditure.  But I think the problem with this approach is that the more complex formula is merely a regression fit of a population of varying individuals, none of whom are you.  That is, even two different people of exactly the same weight, height, age, gender, and activity level do not necessarily burn calories at the same rate.

But even if you don’t know your personal value of \alpha ahead of time, you can estimate it, by measuring calories in (c_n) and actual weight (w_n) for a few weeks, and then finding the corresponding \alpha that yields a sequence of predicted weights (\hat{w}_n) that best fits the actual weights over that same time period, in a least-squares sense.

The following figure shows how this works: as time progresses along the x-axis, and we collect more and more data points, the y-axis indicates the corresponding best estimate of \alpha so far.

Estimating burn rate (alpha) over time.  Early estimates are overwhelmed by the noisy weight measurements.

Estimating burn rate (alpha) over time. Early estimates are overwhelmed by the noisy weight measurements.

Here we can see the effect of the noisiness of the measured actual weights; it can take several weeks just to get a reasonably settled estimate.  But keep in mind that we don’t necessarily need to be trying to lose weight during this time.  This estimation approach should still work just as well whether we are losing, maintaining, or even gaining weight.  But once we have a reasonably accurate “personal” value for \alpha, then we can predict future weight changes assuming any particular planned diet and exercise schedule.

(One final note: recall the constant 0.63 multiplier in the calculation of calories burned per mile run.  I had hoped that I could estimate this value as well using the same approach… but the measured weights turned out to be simply too noisy.  That is, the variability in the weights outweighs the relatively small contribution of running to the weight loss on any given day.)

Edit: In response to several requests for a more detailed description of a procedure for estimating \alpha, I put together a simple Excel spreadsheet demonstrating how it works.  It is already populated with the time series of my recorded weight, calories, and miles from this experiment (see the Appendix below) as an example data set.

Given a particular calories/pound value for \alpha, you can see the resulting sequence of predicted weights, as well as the sum of squared differences (SSE) between these predictions and the corresponding actual measured weights.

Or you can estimate \alpha by minimizing SSE.  This can either be done “manually” by simply experimenting with different values of \alpha (12.0 is a good starting point) and observing the resulting SSE, trying to make it as small as possible; or automatically using the Excel Solver Add-In.  The following figure shows the Solver dialog in Excel 2010 with the appropriate settings.

Excel Solver dialog showing the desired settings to estimate alpha minimizing SSE.

Excel Solver dialog showing the desired settings to estimate alpha minimizing SSE.

Conclusions and open questions

I learned several interesting things from this experiment.  I learned that it is really hard to accurately measure calories consumed, even if you are trying.  (Look at the box and think about this the next time you pour a bowl of cereal, for example.)  I learned that a chicken thigh loses over 40% of its weight from grilling.  And I learned that, somewhat sadly, mathematical curiosity can be an even greater motivation than self-interest in personal health.

A couple of questions occur to me.  First, how robust is this sort of prediction to abrupt changes in diet and/or exercise?  That is, if you suddenly start eating 2500 calories a day when you usually eat 2000, what happens?  What about larger, more radical changes?  I am continuing to collect data in an attempt to answer this, so far with positive results.

Also, how much does the burn rate \alpha vary over the population… and even more interesting, how much control does an individual have over changing his or her own value of \alpha?  For example, I intentionally paid zero attention to the composition of fat, carbohydrates, and protein in the calories that I consumed during this experiment.  I ate cereal, eggs, sausage, toast, tuna, steak (tenderloins and ribeyes), cheeseburgers, peanut butter, bananas, pizza, ice cream, chicken, turkey, crab cakes, etc.  There is even one Chipotle burrito in there.

But what if I ate a strict low-carbohydrate, high-fat “keto” diet, for example?  Would this have the effect of increasing \alpha, so that even for the same amount of calories consumed, I would lose more weight than if my diet were more balanced?  Or is it simply hard to choke down that much meat and butter, so that I would tend to decrease c_n, without any effect on \alpha, but with the same end result?  These are interesting questions, and it would be useful to see experiments similar to this one to answer them.

Appendix: Data collection

The following table shows my measured actual weight in pounds over the course of the experiment:

Mon     Tue     Wed     Thu     Fri     Sat     Sun
251.8   251.6   250.6   249.8   248.4   249.8   249.0
250.4   249.0   247.8   246.6   246.6   247.8   246.2
246.6   244.0   244.6   243.6   243.6   244.0   244.8
242.0   240.6   240.4   240.2   240.2   239.4   238.6
238.0   238.0   237.6   238.0   238.0   238.6   238.6
237.4   239.0   237.6   235.8   236.0   235.0   236.0
233.8   232.4   232.6   233.4   233.4   232.0   233.2
232.6   231.6   232.2   232.2   231.2   231.2   229.6
229.6   229.6   230.6   230.4   229.8   228.0   227.4
227.6   226.2   226.4   225.6   225.8   225.8   226.0
228.0   225.8   225.4   224.6   223.8

The following table shows my daily calorie intake:

Mon     Tue     Wed     Thu     Fri     Sat     Sun
1630    1730    1670    1640    2110    2240    1980
1630    1560    1690    1700    2010    1990    2030
1620    1710    1590    1710    2180    2620    2100
1580    1610    1610    1620    1690    2080    1930
1620    1680    1610    1610    1810    2550    2430
1710    1660    1630    1710    1930    2470    1970
1660    1750    1710    1740    2020    2680    2100
1740    1750    1750    1610    1990    2290    1940
1950    1700    1730    1640    1820    2230    2280
1740    1760    1780    1650    1900    2470    1910
1570    1740    1740    1750

And finally, the following table shows the number of miles run on each day:

Mon     Tue     Wed     Thu     Fri     Sat     Sun
2.5     0.0     2.5     0.0     0.0     2.5     0.0
2.5     0.0     2.5     0.0     0.0     2.5     0.0
2.5     0.0     2.5     0.0     0.0     3.0     0.0
2.5     0.0     2.5     0.0     0.0     3.0     0.0
2.5     0.0     3.0     0.0     0.0     3.0     0.0
2.5     0.0     3.0     0.0     0.0     3.0     0.0
3.0     0.0     3.0     0.0     0.0     3.0     0.0
3.0     0.0     3.0     0.0     0.0     3.0     0.0
3.0     0.0     3.0     0.0     0.0     3.5     0.0
3.0     0.0     3.0     0.0     0.0     3.5     0.0
3.0     0.0     3.5     0.0
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No, diversity does not generally trump ability

I am pretty sure that my motivation for this post is simply sufficient annoyance.  I admittedly have a rather harsh view of social “science” in general.  But this particular study seems to have enough visibility and momentum that I think it’s worth calling attention to a recent rebuttal.  Where by “rebuttal” I mean “brutal takedown.”

At issue is a claim by Lu Hong and Scott Page, including empirical evidence from computer simulation and even a mathematical “proof,” that “diversity trumps ability.”  The idea is that when comparing performance of groups of agents working together to solve a problem, groups selected randomly from a “diverse” pool of agents of varying ability can perform better than groups comprised solely of the “best” individuals.

“Diversity” is a fun word.  It’s a magnet for controversy, particularly when, as in this case, it is conveniently poorly defined.  But the notion that diversity might actually provably yield better results is certainly tantalizing, and is worth a close look.

Unfortunately, upon such closer inspection, Abigail Thompson in the recent AMS Notices shows that not only is the mathematics in the paper incorrect, but even when reasonably corrected, the result is essentially just a tautology, with little if any actual “real world” interpretation or application.  And the computer simulation, that ostensibly provides backing empirical evidence, ends up having no relevance to the accompanying mathematical theorem.

The result is that Hong and Page’s central claim enjoys none of the rigorous mathematical justification that distinguished it from most of the literature on diversity research in the first place.  And this is what annoys me: trying to make an overly simple-to-state claim– that is tenuous to begin with– about incredibly complex human behavior, and dressing it up with impressive-sounding mathematics.  Which turns out to be wrong.

References:

1. Hong, L. and Page, S., Groups of diverse problem solvers can outperform groups of high-ability problem solvers, Proc. Nat. Acad. of Sciences, 101(46) 2004 [PDF]

2. Thompson, Abigail, Does Diversity Trump Ability? An Example of the Misuse of Mathematics in the Social Sciences, Notices of the AMS, 61(9) 2014, p. 1024-1030 [PDF]

 

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The discreet hotel manager

The following puzzle deals with storage and search strategies for querying membership of elements in a table.  Like the light bulb puzzle, it’s an example of a situation where binary search is only optimal in the limiting case.  I will try to present the problem in the same weirdly racy context in which I first encountered it:

You are the manager of a hotel that, once a year, hosts members of a secretive private club in n=10 numbered suites on a dedicated floor of the hotel.  There are m=18 club members, known to you only by numbers 1 through m.  Each year, some subset of 10 of the 18 club members come to the hotel for a weekend, each staying in his own suite.  To preserve secrecy, you assign each man to a suite… but then destroy your records, including the list of the particular members staying that weekend.

It may be necessary to determine whether a particular member is staying at the hotel (say, upon receiving a call from his wife asking if her husband is there).  But the club has asked that in such cases you must only knock on the door of a single suite to find out who is residing there.  For a given subset of 10 club members, how should you assign them to suites, and what is your “single-query” search strategy for determining whether any given club member is staying at your hotel?

This problem is interesting because, for a sufficiently large universe m of possible key values, we can minimize the maximum number of queries needed in a table of size n by storing the n keys in sorted order, and using binary search, requiring \lceil lg(n+1) \rceil queries in the worst case.  But for small m, as in this problem, we can do better.  For example, if m=n, then we don’t need any queries, since every key is always in the table.  If m=n+1, then we can store in a designated position the key immediately preceding (cyclically if necessary) the missing one, requiring just a single query to determine membership.

(Hint: The problem is setup to realize a known tight bound: if m \leq 2(n-1), then there is a strategy requiring just a single query in the worst case.)

 

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Probabilistic Tic-Tac-Toe solution

This is a follow-up to the previous post about a Hollywood Squares-style game of probabilistic Tic-Tac-Toe, where a selected square is marked with the player’s symbol (say, X) with some fixed probability p, but with the opponent’s symbol (O) with probability 1-p.  When p=1 this reduces to the standard game of Tic-Tac-Toe, in which neither player has an advantage; that is, optimal strategy by both players results in a draw.  But what if p<1?

Without any additional rules, analysis of this simplest form of the game is pretty straightforward.  The green curve in the figure below shows the first player’s advantage as a function of p (assuming the loser pays the winner a dollar).

First player's advantage vs. probability p of "success" of any given move.

First player’s advantage vs. probability p of “success” of any given move.

Recall that the parameter p effectively models the level of difficulty of the trivia questions asked in the television versions of the game.  Interestingly, hard questions seem to be significantly more beneficial for the second player than easy questions are for the first player.

As mentioned last week, the television games prevent ties with the additional rule that a player can win by either getting three in a row, or being the first to get any five squares.  The black curve above shows the effect of this incremental modification, which behaves about like we would expect.

Finally, the most interesting additional rule is that a player can’t “lose” the game by missing a question; he or she must “win” by answering correctly.  If a miss would result in a win for the opponent, the selected square remains empty and the player simply loses his turn.  In this case, we must be careful to handle potentially repeating game states: for example, suppose that the board looks like this:

On either player's turn, he or she must answer correctly to win; a miss is simply a lost turn.

On either player’s turn, he or she must answer correctly to win; a miss is simply a lost turn.

If player X misses on his turn, and player O also misses on her turn, then we are right back where we started.  We can eliminate this “infinite recursion” by expressing the value of the board for a given player in terms of itself, and solving:

v = p + (1-p)(-p + (1-p)v)

v = \frac{p}{2-p}

The blue and red curves show the effect of this rule, with and without the “first to five” tie-breaker rule; the blue curve represents the television version of the game.  The first player has an advantage for all but the most difficult questions, and has a huge advantage for relatively easy questions– it would be interesting to see an estimate of the “actual” value of p based on the historical fraction of questions answered correctly in episodes of the show.

 

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Probabilistic Tic-Tac-Toe

This past week, I was presented with what I thought was a very interesting problem: what is the optimal strategy in the game Tic-Tac-Toe?

Okay, so maybe that’s not actually a very interesting problem.  Although I think it can be a great first “game AI” programming exercise for students, it is well known that the game is “fair” in the sense that optimal play by both players results in a draw.

But motivated by the recent revival of the British television game show Celebrity Squares (which is in turn based on the original American show Hollywood Squares), let’s incrementally add a few twists to Tic-Tac-Toe that do make things more interesting:

Probabilistic moves

First, make moves “probabilistically.”  That is, when a player chooses a square in which to place his marker (say X), then with probability p the move is actually successful, but with probability 1-p he “loses” the square and his opponent places her marker (O) in that square instead.  This models the situation on the television game show, where a player must essentially know the correct answer to a trivia question to successfully place his mark in a chosen square.  By fixing p we are effectively assuming that the two players are evenly matched, and that all questions are of equal difficulty, with larger values of p corresponding to easier questions (so that when p=1 the game reduces to the original deterministic Tic-Tac-Toe).

How fair is this game?  That is, as a function of p, what is the optimal strategy and resulting expected value for each player (assuming the loser pays the winner a dollar)?

Forbidden “stolen” wins

An additional rule in the television game show is that a player can only win while “in control” of the board.  That is, for example, if player X answers a question incorrectly, then an O is placed in the square… unless the result would be an immediate win for player O, in which case the square remains unmarked, and player X merely loses his turn.

This rule is interesting not only because of the effect it has on the fairness of the game, but also because it introduces a tricky wrinkle in computing strategies and expected values.  The problem is that it is now possible for game states to repeat; for example, if both players can win on their next move with the same square, and player X answers incorrectly, followed by player O also answering incorrectly, then we are right back where we started.

First to five squares

Finally, note that so far it has still been possible for a game to end in a draw.  To prevent the uncomfortable awkward silence that I imagine resulting from a tie at the end of a game show (Jeopardy! being an exception), there is one additional rule that guarantees an outright winner: a player can win by either getting three in a row, or by being the first to mark any five squares.

(For completeness, we can also consider a slight variation of this rule that was in effect in the early episodes of the American show, where a player could only win with five or more squares once the board was full– or equivalently, only once his opponent had no potential three in a row opportunities.)

Taking all of these rules together– or considering various combinations of them– what is the resulting optimal strategy and expected value for this game?  I’m not sure yet… but it seems like a fun problem to try to solve.

 

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Solving a corn maze

Every fall for the last several years, my wife and I have walked through a corn maze with our nephews.  It’s a lot of fun; the kids get a paper copy of the maze to help them navigate, and there are a series of numbered checkpoints to find, each with a themed quiz question to answer.  (I thought this was a pretty good idea, giving a clear sense of continuing progress, rather than just wandering through a maze of twisty little passages, all alike.)

But what if you don’t have a map of the maze?  There are many algorithms for “solving” a maze, with various requirements on the structure of the maze and the assumed ability of the solver to “record” information about parts of the maze already explored.  Some only work on a grid, while others, such as wall-following, are only guaranteed to work if the maze is simply connected– or in graph-theoretic terms, the graph representing the rooms (vertices) and passages (edges) of the maze must not contain any cycles.

Suppose instead that you find yourself in a maze of rooms and connecting passages like the one shown below, whose corresponding graph is neither a grid, nor acyclic.

A maze with 20 rooms and 30 connecting passages.

A maze with 20 rooms and 30 connecting passages.

In fact, the maze need not even be planar (think catacombs with passages running over/under each other), and it may contain loops (passages that lead you back to the room you  just left) or multiple passages between the same pair of rooms.  The only requirement is that each passage has two ends– imagine a door at each end leading into a room… although as in the case of a loop, both doors may lead into the same room.

Now imagine exploring the maze by following these rules:

  1. If you are in a room with an unmarked door (as you will be at the start), pick one, mark an X on it, and traverse the corresponding passage.
  2. When first entering a room with all doors unmarked, mark a Y on the door of entry.
  3. If you are in a room with all doors marked, exit via the door marked Y if there is one, otherwise stop.

This algorithm, due to Tarry (see reference below), will visit every room in the (connected) maze, traversing every passage exactly twice, once in each direction… ending in the same room where you started!  Proving this is a nice problem involving induction.  And the doors marked with a Y have the nice property of essentially marking a path from any room back to the starting point.  (As an interesting side effect, suppose that we modify rule 3 slightly, so that before exiting a door marked Y, we remove or erase all of the marks on the doors in that room.  The result is that, at the end, we have still visited every room in the maze, but erased any trace that we were there!)

Tarry’s algorithm has a lot in common with Trémaux’s algorithm, although I think this version is more explicitly “local,” in the sense that I can imagine actually executing Tarry’s algorithm in, say, a corn maze, with appropriate markings on the “doors” at intersections, as opposed to needing to mark the passages between intersections.

References:

1. Tarry G., Le problème des labyrinthes.  Nouv. Ann. Math., 14 (1895), 187-190

 

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Projectile motion puzzle solution

This post captures my notes on the following problem from the last post:

Problem: Suppose that you are standing on the outer bank of a moat surrounding a castle, and you wish to secretly deliver a message, attached to a large rock, to your spy inside the castle.  A wall h=11 meters high surrounds the castle, which is in turn surrounded by the moat which is d=19 meters wide.  At what angle should you throw the rock in order to have the best chance of clearing the wall?

Solution: A projectile thrown from the origin with initial speed v at an angle \theta has a trajectory described by

x(t) = v t \cos \theta

y(t) = v t \sin \theta - \frac{1}{2} g t^2

Setting x(t)=d and y(t)=h and solving for v and t, we find the following expression for the required initial speed as a function of the throwing angle:

v(\theta)^2 = \frac{g d^2}{2 \cos^2 \theta (d \tan \theta - h)}

We can minimize this required initial speed by maximizing the denominator.  Differentiating and setting equal to zero yields

\tan 2\theta = -\frac{d}{h}

Keeping in mind that the throwing angle is between 0 and 90 degrees, we can rewrite this expression as

\theta = \frac{\phi + \pi/2}{2}

where \phi is the angle of the line of sight to the top of the wall.  The geometric interpretation of this result is: to have the best chance of clearing the wall, throw at an angle halfway between the line of sight to the top of the wall and the vertical.  In the original problem, this optimal throwing angle is about 60.03 degrees.

Recall, however, that this analysis assumes negligible effects of air resistance.  This is a safe assumption for a relatively heavy rock at human-throwable speeds, but not for a baseball.  If we apply this same analysis to Jackie Bradley, Jr.’s throw from home plate over the center field wall, 420 feet away and 17 feet high, we get an optimal throwing angle of about 46.2 degrees, with a minimum initial speed of only about 80.9 miles per hour, which is not terribly difficult to achieve.

To more accurately model the trajectory of a thrown (or batted) baseball, we must incorporate the effects of not only drag, which slows the ball down, but also the Magnus force, which “lifts” the ball causing it to “rise” and/or curve.  The references below describe several interesting experiments tracking thrown and hit baseballs, including some useful approximation formulas for accurately modeling these trajectories, yielding my estimate from the last post that Bradley must have thrown the ball at over 105 miles per hour, with an optimal throwing angle of just a little over 30 degrees.

Since this is the sort of problem that I think is excellent for students to attack via computer simulation, following are the relevant formulas and constants collected in one place for them to use:

The acceleration of a thrown or batted baseball due to gravity, drag, and lift is given by

a = g + a_D + a_L

a_D = -\frac{1}{2m}\rho v^2 A C_D \hat{v}

a_L = \frac{1}{2m}\rho v^2 A C_L (\hat{\omega} \times \hat{v})

where:

  • Acceleration due to gravity |g| = 9.80665 meters/second^2
  • Mass m = 5.125 ounces (the midpoint of the regulation tolerance)
  • Air density \rho = 1.225 kilograms/meter^3 (sea level on a standard day)
  • v and \omega are the magnitudes of velocity and angular velocity, respectively, with \hat{v} and \hat{\omega} being corresponding unit vectors in the same direction
  • A = \pi r^2 is the cross-sectional area of the baseball, where the circumference 2\pi r = 9.125 inches (the midpoint of the regulation tolerance)
  • Coefficient of drag C_D = 0.35 (see Reference 3)
  • Coefficient of lift C_L = S/(0.4+2.32S), where S = r\omega/v is the “spin parameter” (see Reference 2)
  • Backspin on a typical fastball \omega = 1500 rpm (or 2000 rpm for a typical batted ball; see Reference 1)

 

References:

1. A. Nathan, The effect of spin on the flight of a baseball, American Journal of Physics, 76:2 (February 2008), 119-124 [PDF]

2. A. Nathan, What New Technologies Are Teaching Us About the Game of Baseball, October 2012 [PDF]

3. D. Kagan and A. Nathan, Simplified Models for the Drag Coefficient of a Pitched Baseball, The Physics Teacher, 52 (May 2014), 278-280 [PDF]

 

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