Negative D2 score on training data after lassoglm fit

How can the deviance from a null model (i.e. betas all equal zero) be lower than the deviance from the full model? Surely lassoglm should choose betas all zero in this case?

From the code below, my d2Train is -0.0808.

[B, FitInfo] = lassoglm(table2array(indat.params.trainDataX), indat.params.trainDataY(:, minInd), 'poisson', 'Lambda', indat.combTable.bestLambdas(minInd), 'Alpha', indat.combTable.bestAlphas(minInd));

predCountsTrain = calculateRates(table2array(indat.params.trainDataX),B,FitInfo.Intercept)+eps;
predDevianceTrain = calculateDeviance(indat.params.trainDataY(:, minInd),predCountsTrain);
nullCountsTrain = calculateRates(table2array(indat.params.trainDataX),zeros(size(B)),FitInfo.Intercept)+eps;
nullDevianceTrain  = calculateDeviance(indat.params.trainDataY(:, minInd),nullCountsTrain);
d2Train = 1 - (predDevianceTrain ./ nullDevianceTrain);

function rates = calculateRates(x,y,int)
rates = exp((x * y) + int);
end

function dev = calculateDeviance(observed,predicted)
    scaledLogRatio = log(observed./predicted).*observed;
    rawDifference = observed-predicted;
    diffOfTerms = scaledLogRatio - rawDifference;
    dev = nansum(diffOfTerms)*2;
end