This is the query:

SELECT 
  races.*, tmptimers.last_start_time, tmplaps.updated_at AS last_updated_at
FROM 
  races
LEFT JOIN 
  (SELECT * FROM timers WHERE timers.user_id = 1) AS tmptimers ON tmptimers.race_id = races.id
LEFT JOIN
  (
  SELECT 
    race_id, 
    MAX(updated_at) AS updated_at 
  FROM
    (SELECT 
      race_id, updated_at
    FROM 
      laps 
    WHERE 
      user_id = 148
      AND updated_at > '2014-06-13'
    ) AS tmp
    GROUP BY 
    race_id
  ) AS tmplaps ON tmplaps.race_id = races.id
WHERE
  races.account_id = 5
  AND races.id IN (29, 30, 31, 32, 3, 2, 33, 1, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 
    22, 23, 24, 25, 26, 27, 28, 34, 35, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50, 51, 52, 53, 54, 
    55, 56, 57, 58, 59, 60, 61, 62, 63, 64, 65, 66, 119, 154, 122, 82, 123, 156, 76, 73, 138, 70, 150, 135, 92, 143, 
    75, 72, 126, 113, 80, 83, 77, 94, 87, 129, 117, 68, 104, 134, 148, 152, 111, 120, 141, 69, 158, 96, 116, 114, 
    147, 78, 130, 139, 131, 90, 118, 132, 136, 67, 102, 84, 105, 101, 81, 153, 112, 137, 144, 71, 88, 89, 157, 107, 
    109, 79, 140, 97, 110, 106, 93, 142, 128, 108, 151, 95, 98, 121, 103, 149, 85, 124, 145, 99, 125, 146, 115, 133,
    100, 86, 91, 127, 155, 74)
ORDER BY
  last_updated_at DESC;

And here's the explain analyze for my (rather small) dataset:

   Sort Key: tmplaps.updated_at
   Sort Method: quicksort  Memory: 47kB
   ->  Nested Loop Left Join  (cost=211.16..286.25 rows=158 width=130) (actual time=3.854..4.248 rows=158 loops=1)
         Join Filter: (timers.race_id = races.id)
         ->  Hash Left Join  (cost=211.16..265.50 rows=158 width=122) (actual time=3.852..4.186 rows=158 loops=1)
               Hash Cond: (races.id = tmplaps.race_id)
               ->  Seq Scan on races  (cost=0.00..52.81 rows=158 width=114) (actual time=0.012..0.254 rows=158 loops=1)
                     Filter: ((account_id = 5) AND (id = ANY ('{29,30,31,32,3,2,33,1,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,119,154,122,82,123,156,76,73,138,70,150,135,92,143,75,72,126,113,80,83,77,94,87,129,117,68,104,134,148,152,111,120,141,69,158,96,116,114,147,78,130,139,131,90,118,132,136,67,102,84,105,101,81,153,112,137,144,71,88,89,157,107,109,79,140,97,110,106,93,142,128,108,151,95,98,121,103,149,85,124,145,99,125,146,115,133,100,86,91,127,155,74}'::integer[])))
                     Rows Removed by Filter: 3
               ->  Hash  (cost=209.96..209.96 rows=96 width=12) (actual time=3.833..3.833 rows=96 loops=1)
                     Buckets: 1024  Batches: 1  Memory Usage: 5kB
                     ->  Subquery Scan on tmplaps  (cost=208.03..209.96 rows=96 width=12) (actual time=3.768..3.807 rows=96 loops=1)
                           ->  HashAggregate  (cost=208.03..209.00 rows=96 width=12) (actual time=3.766..3.786 rows=96 loops=1)
                                 ->  Seq Scan on laps  (cost=0.00..182.03 rows=5201 width=12) (actual time=0.005..2.075 rows=5202 loops=1)
                                       Filter: ((updated_at > '2014-06-13 00:00:00'::timestamp without time zone) AND (user_id = 148))
         ->  Materialize  (cost=0.00..18.38 rows=1 width=12) (actual time=0.000..0.000 rows=0 loops=158)
               ->  Seq Scan on timers  (cost=0.00..18.38 rows=1 width=12) (actual time=0.001..0.001 rows=0 loops=1)
                     Filter: (user_id = 1)
 Total runtime: 4.494 ms

It seems to me that the most critical piece is the SeqScan on laps. Is there anything that can be done to minimize the time it takes?