We present the results of the first strong lens time delay challenge .
The motivation , experimental design , and entry level challenge are described in a companion paper .
This paper presents the main challenge , TDC1 , which consisted of analyzing thousands of simulated light curves blindly .
The observational properties of the light curves cover the range in quality obtained for current targeted efforts ( e.g. , COSMOGRAIL ) and expected from future synoptic surveys ( e.g. , LSST ) , and include simulated systematic errors .
Seven teams participated in TDC1 , submitting results from 78 different method variants .
After a describing each method , we compute and analyze basic statistics measuring accuracy ( or bias ) A , goodness of fit \chi ^ { 2 } , precision P , and success rate f .
For some methods we identify outliers as an important issue .
Other methods show that outliers can be controlled via visual inspection or conservative quality control .
Several methods are competitive , i.e. , give |A| < 0.03 , P < 0.03 , and \chi ^ { 2 } < 1.5 , with some of the methods already reaching sub-percent accuracy .
The fraction of light curves yielding a time delay measurement is typically in the range f = 20–40 % .
It depends strongly on the quality of the data : COSMOGRAIL-quality cadence and light curve lengths yield significantly higher f than does sparser sampling .
Taking the results of TDC1 at face value , we estimate that LSST should provide around 400 robust time-delay measurements , each with P < 0.03 and |A| < 0.01 , comparable to current lens modeling uncertainties .
In terms of observing strategies , we find that A and f depend mostly on season length , while P depends mostly on cadence and campaign duration .